CN115309613B - Method and system for selecting auxiliary edge node by running monitoring chip - Google Patents

Method and system for selecting auxiliary edge node by running monitoring chip Download PDF

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CN115309613B
CN115309613B CN202211240117.8A CN202211240117A CN115309613B CN 115309613 B CN115309613 B CN 115309613B CN 202211240117 A CN202211240117 A CN 202211240117A CN 115309613 B CN115309613 B CN 115309613B
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CN115309613A (en
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王嘉诚
张少仲
张栩
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Zhongcheng Hualong Computer Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3024—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a central processing unit [CPU]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3051—Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3089—Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents
    • G06F11/3093—Configuration details thereof, e.g. installation, enabling, spatial arrangement of the probes

Abstract

The invention discloses a method and a system for selecting an auxiliary edge node by an operation monitoring chip, which relate to the chip application technology, wherein the method comprises the following steps: determining a first task matching degree of the first edge node and each auxiliary edge node when the running deviation degree of the first edge node between the current running period and the last running period is larger than a deviation degree threshold; selecting candidate edge nodes from edge nodes of the same data domain; determining a dynamic matching degree threshold value based on the first task matching degree of the first edge node and each auxiliary edge node, and removing the auxiliary edge nodes of which the first task matching degree is smaller than the dynamic matching degree threshold value from the auxiliary node set; and calculating a second task matching degree of each candidate edge node and the first edge node, and selecting at least one auxiliary edge node from the plurality of candidate edge nodes based on the second task matching degree. According to the scheme, the response time can be reduced, and the overall processing efficiency of the edge nodes in the data domain is improved.

Description

Method and system for selecting auxiliary edge node by running monitoring chip
Technical Field
The present invention relates to chip application technologies, and more particularly, to a method and system for selecting an auxiliary edge node by an operation monitoring chip.
Background
Currently, in an edge computing system, in order to prevent each or any edge node or edge computing node from entering a long-term high-load state, and in order to enable each or any edge node to send or migrate a task to another edge node when receiving the task that cannot be processed in time or cannot be processed, a logically auxiliary edge node may be established for each or any edge node. And the auxiliary edge node is used for performing auxiliary processing on the tasks which cannot be processed or cannot be processed by the edge node in time. However, when the operation state of a specific edge node changes, if the logical secondary edge node is not updated or replaced, the load of the secondary edge node may be in a higher state when the load of the specific edge node is higher. In this case, if a task that cannot be processed or cannot be processed in time at a specific edge node is sent or migrated to a secondary edge node, the load of the secondary edge node is too high, so as to increase the response time of task processing, and even cause task congestion at the secondary edge node until the system crashes.
Disclosure of Invention
In order to solve the problems in the prior art, the method and the device determine whether the running deviation degree of the selected or specific edge node between two running periods is greater than a deviation degree threshold value by utilizing the node running information of two adjacent running periods. When the deviation degree is greater than the deviation degree threshold value, the operation state of the selected or specific edge node is obviously or substantially changed, for example, the time interval with higher load rate is obviously or substantially changed. To this end, with such state decisions being obtained with significant technical improvements over the prior art, the present application requires that the secondary edge nodes be reselected by the operation monitoring chip for selected or specific edge nodes.
According to an aspect of the present invention, there is provided a method of selecting a secondary edge node by an operation monitoring chip, the method comprising:
when the current operation period is finished, the operation monitoring chip of the first edge node determines the operation deviation degree of the first edge node between the current operation period and the previous operation period based on the node operation information of the current operation period and the node operation information of the previous operation period;
determining a first task match score for a first edge node and each auxiliary edge node in a set of auxiliary nodes associated with the first edge node when the running deviation score is greater than a deviation score threshold;
selecting edge nodes which are not auxiliary edge nodes from all edge nodes except the first edge node and belong to the same data domain as the first edge node as candidate edge nodes;
determining a dynamic matching degree threshold value based on the first task matching degree of the first edge node and each auxiliary edge node, and removing the auxiliary edge nodes of which the first task matching degree is smaller than the dynamic matching degree threshold value from the auxiliary node set; and
calculating a second task matching degree of each candidate edge node and the first edge node, selecting at least one auxiliary edge node from the candidate edge nodes based on the second task matching degree, and adding the selected at least one auxiliary edge node to an auxiliary node set of the first edge node;
further comprising, for each operating cycle:
the operation monitoring chip generates a periodic task record for each periodic task of the first edge node, wherein the periodic task record comprises: the method comprises the steps that task identification, a running period sequence number, task starting time, task ending time and task weight are carried out, and a plurality of periodic task records form periodic task information;
the operation monitoring chip generates an aperiodic task record for each aperiodic task in the first edge node, wherein the aperiodic task record comprises: recording a plurality of non-periodic tasks to form non-periodic task information;
the operation monitoring chip records the load of the first edge node in each time unit of the operation period to form load statistical information; and
and the operation monitoring chip forms the node operation information of the operation period by the periodic task information, the aperiodic task information and the load statistical information in the operation period.
Preferably, the determining, by the operation monitoring chip of the first edge node, the operation deviation degree of the first edge node between the current operation cycle and the previous operation cycle based on the node operation information of the current operation cycle and the node operation information of the previous operation cycle includes:
the operation monitoring chip of the first edge node acquires periodic task information, aperiodic task information and load statistical information of the current operation period from the node operation information of the current operation period;
the operation monitoring chip acquires node operation information of a previous operation period, and acquires periodic task information, aperiodic task information and load statistical information of the previous operation period from the node operation information of the previous operation period;
determining a first deviation degree based on the periodic task information of the previous operation period and the periodic task information of the current operation period;
determining a second deviation degree based on the aperiodic task information of the previous operation period and the aperiodic task information of the current operation period;
determining a third deviation degree based on the load statistical information of the previous operation period and the load statistical information of the current operation period;
and determining the operation deviation degree of the first edge node between the current operation period and the last operation period based on the first deviation degree, the second deviation degree and the third deviation degree.
Preferably, wherein the determining the first deviation degree based on the periodic task information of the previous operation cycle and the periodic task information of the current operation cycle comprises:
acquiring a plurality of periodic task records of the previous operating period from the periodic task information of the previous operating period;
acquiring a plurality of periodic task records of the current operation period from the periodic task information of the current operation period;
forming a periodic task record pair by two periodic task records with the same task identifier in the previous operating period and the current operating period based on the operating period sequence number;
determining a periodic time difference value ratio between the last operating period and the current operating period of the same periodic task with the same task identification based on each periodic task record pair;
a first degree of deviation is determined based on the periodic time difference ratio and the task weight.
Preferably, wherein determining, based on each periodic task record pair, a periodic time difference value ratio between a last operation cycle and a current operation cycle of the same periodic task with the same task identification comprises:
extracting the task starting time and the task ending time of the same periodic task in the last operating period and the task starting time and the task ending time in the current operating period from each periodic task record pair;
determining a periodic time difference value ratio between the last operating period and the current operating period of the same periodic task with the same task identification based on the following formula:
Figure 269283DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 736824DEST_PATH_IMAGE002
for the periodic time difference ratio between the last operating cycle and the current operating cycle for the ith periodic task, PBT i The task starting time of the ith periodic task in the last operating period is set; PET i The task ending time of the ith periodic task in the last operating period is defined; CBT i The task starting time of the ith periodic task in the current operation period is set; CET (CET) i The task ending time of the ith periodic task in the current operation period;
Figure 110036DEST_PATH_IMAGE003
i and np are natural numbers and np is the number of periodic tasks.
Preferably, wherein the determining the first degree of deviation based on the periodic time difference value ratio and the task weight comprises:
determining a task weight of each periodic task based on a plurality of periodic task records of a current operation period;
determining a first degree of deviation based on the following equation:
Figure 770825DEST_PATH_IMAGE004
wherein, the first and the second end of the pipe are connected with each other,
Figure 54038DEST_PATH_IMAGE005
in order to be the first degree of deviation,
Figure 220709DEST_PATH_IMAGE006
is the task weight of the ith periodic task.
Preferably, the determining the second deviation degree based on the aperiodic task information of the previous operation period and the aperiodic task information of the current operation period includes:
acquiring a plurality of aperiodic task records of the previous operating period from the aperiodic task information of the previous operating period, and determining the number of aperiodic tasks in the previous operating period and a first aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the previous operating period;
acquiring a plurality of aperiodic task records of the current operation period from the aperiodic task information of the current operation period, and determining the number of aperiodic tasks in the current operation period and a second aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the current operation period;
determining a quantity threshold value based on the quantity of the non-periodic tasks and the first non-periodic tasks in the last operation period, and the quantity of the non-periodic tasks and the second non-periodic tasks in the current operation period;
determining at least one high concurrent operating time interval of the previous operating cycle based on the plurality of aperiodic task records of the previous operating cycle and the quantity threshold, and determining at least one high concurrent operating time interval of the current operating cycle based on the plurality of aperiodic task records of the current operating cycle and the quantity threshold;
determining a second degree of deviation based on the at least one high concurrent runtime interval of the last operating cycle and the at least one high concurrent runtime interval of the current operating cycle.
Preferably, wherein determining at least one high concurrency runtime interval for a previous run cycle based on a plurality of aperiodic task records for the previous run cycle and the number threshold comprises:
generating a task concurrent running graph of a plurality of non-periodic tasks of the previous running period based on the task starting time and the task ending time in the plurality of non-periodic task records of the previous running period;
in the task concurrent operation graph of the last operation period, at least one high concurrent operation time interval in which the number of the non-periodic tasks which are concurrently operated is greater than or equal to the number threshold is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
Preferably, wherein determining at least one high concurrency runtime interval for the current operating cycle based on the number of aperiodic task records for the current operating cycle and the number threshold comprises:
generating a task concurrent running graph of a plurality of aperiodic tasks of the current running period based on the task starting time and the task ending time in a plurality of aperiodic task records of the current running period;
in the task concurrent operation graph of the current operation cycle, at least one high concurrent operation time interval with the quantity of the non-periodic tasks which are operated concurrently being larger than or equal to the quantity threshold value is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
Preferably, wherein the determining the third deviation degree based on the load statistical information of the previous operation cycle and the load statistical information of the current operation cycle comprises:
determining a load record in each time unit of the last operation cycle according to the load statistical information of the last operation cycle, determining an average processor utilization rate in each time unit according to the load record in each time unit of the last operation cycle, and determining the first time unit number of the time units of which the average processor utilization rate is greater than a utilization rate threshold value based on the average processor utilization rate in each time unit of the last operation cycle;
determining a load record in each time unit of the current operation period according to the load statistical information of the current operation period, determining an average processor utilization rate of each time unit according to the current operation period according to the load record in each time unit of the current operation period, and determining the number of second time units of the time units of which the average processor utilization rate is greater than a utilization rate threshold value based on the average processor utilization rate of each time unit of the current operation period;
determining the number of time units of the previous operation period and the number of time units of the current operation period; and
and determining a third deviation degree based on the number of the first time units, the number of the time units of the previous operation period, the number of the second time units and the number of the time units of the current operation period.
Preferably, wherein determining the operation deviation degree of the first edge node between the current operation cycle and the previous operation cycle based on the first deviation degree, the second deviation degree and the third deviation degree comprises:
determining a degree of operational deviation based on the following formula
Figure 691004DEST_PATH_IMAGE007
：
Figure 483380DEST_PATH_IMAGE008
Wherein the content of the first and second substances,
Figure 621100DEST_PATH_IMAGE009
is a first degree of deviation of the first degree of deviation,
Figure 958672DEST_PATH_IMAGE010
in order to provide the second degree of deviation,
Figure 181843DEST_PATH_IMAGE011
in order to be the third degree of deviation,
Figure 184434DEST_PATH_IMAGE012
in order to be the first adjustment factor,
Figure 301294DEST_PATH_IMAGE013
in order to be the second adjustment factor,
Figure 199980DEST_PATH_IMAGE014
is a third adjustment factor;
wherein the content of the first and second substances,
Figure 520234DEST_PATH_IMAGE015
，
Figure 264199DEST_PATH_IMAGE016
，
Figure 501146DEST_PATH_IMAGE017
，
Figure 101891DEST_PATH_IMAGE018
。
preferably, wherein determining a first task match score for a first edge node and each secondary edge node in a set of secondary nodes associated with the first edge node comprises:
acquiring load statistical information of the current operation period from the node operation information of the current operation period of the first edge node, acquiring a load record in each time unit of the current operation period of the first edge node according to the load statistical information of the current operation period,
acquiring load statistical information of a current operation period of each auxiliary edge node in an auxiliary node set, and acquiring a load record in each time unit of the current operation period of each auxiliary edge node based on the load statistical information of the current operation period of each auxiliary edge node;
and determining a first task matching degree of the first edge node and each auxiliary edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each auxiliary edge node.
Preferably, the method further comprises determining an associated set of auxiliary nodes for the first edge node in the same data domain in advance, wherein each edge node in the associated set of auxiliary nodes is an auxiliary edge node of the first edge node.
Preferably, the selecting, as the candidate edge node, an edge node that is not an auxiliary edge node among all edge nodes except the first edge node belonging to the same data domain as the first edge node includes:
and selecting edge nodes which are not auxiliary edge nodes in the auxiliary node set from all the edge nodes except the first edge node belonging to the same data domain as the first edge node as candidate edge nodes of the first edge node.
Preferably, wherein determining the dynamic matching degree threshold based on the first task matching degree of the first edge node and each auxiliary edge node comprises:
determining an average value of the plurality of first task matching degrees and a minimum value of the plurality of first task matching degrees based on the first task matching degrees of the first edge node and each auxiliary edge node;
determining a dynamic matching degree threshold value based on the following formula:
Figure 299654DEST_PATH_IMAGE019
wherein Mh is a dynamic matching degree threshold value,
Figure 719747DEST_PATH_IMAGE020
is the average value of the first task matching degrees, and Mina is the minimum value of the first task matching degrees.
Preferably, the calculating the second task matching degree of each candidate edge node with the first edge node includes:
acquiring load statistical information of the current operation period from the node operation information of the current operation period of the first edge node, acquiring a load record in each time unit of the current operation period of the first edge node according to the load statistical information of the current operation period,
acquiring load statistical information of the current operation period of each candidate edge node to acquire a load record in each time unit of the current operation period of each candidate edge node based on the load statistical information of the current operation period of each candidate edge node;
and determining a second task matching degree of the first edge node and each candidate edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each candidate edge node.
Preferably, wherein selecting at least one auxiliary edge node from the plurality of candidate edge nodes based on the second task matching degree comprises:
and selecting the candidate edge node with the second task matching degree larger than the static matching degree threshold value as the auxiliary edge node.
Preferably, the method further comprises, when the first edge node determines that a newly received aperiodic task needs to be scheduled, selecting a second edge node from a plurality of secondary edge nodes in the associated set of secondary nodes;
sending the newly received aperiodic task to the second edge node, whereby the newly received aperiodic task is processed by the second edge node.
Based on another aspect of the embodiments of the present invention, there is provided a system for selecting an auxiliary edge node by an operation monitoring chip, the system including:
the first determining device is used for prompting the operation monitoring chip of the first edge node to determine the operation deviation degree of the first edge node between the current operation period and the previous operation period based on the node operation information of the current operation period and the node operation information of the previous operation period when the current operation period is finished;
second determining means for causing a determination of a first task match for a first edge node and each auxiliary edge node in a set of auxiliary nodes associated with the first edge node when the running deviation is greater than a deviation threshold;
selecting means for selecting, as candidate edge nodes, edge nodes that are not auxiliary edge nodes among all edge nodes except the first edge node belonging to the same data domain as the first edge node;
removing means for determining a dynamic matching degree threshold based on the first task matching degree of the first edge node and each auxiliary edge node, and removing the auxiliary edge nodes of which the first task matching degree is smaller than the dynamic matching degree threshold from the auxiliary node set; and
the adding device is used for calculating a second task matching degree of each candidate edge node and the first edge node, selecting at least one auxiliary edge node from the candidate edge nodes based on the second task matching degree, and adding the selected at least one auxiliary edge node into an auxiliary node set of the first edge node;
recording means for, for each operating cycle: the method comprises the following steps of prompting an operation monitoring chip to generate a periodic task record for each periodic task of a first edge node, wherein the periodic task record comprises: the method comprises the steps that task identification, a running period sequence number, task starting time, task ending time and task weight are carried out, and a plurality of periodic task records form periodic task information; prompting the operation monitoring chip to generate an aperiodic task record for each aperiodic task in the first edge node, wherein the aperiodic task record comprises: recording a plurality of non-periodic tasks to form non-periodic task information; prompting the operation monitoring chip to record the load of the first edge node in each time unit of the operation period to form load statistical information; and prompting the operation monitoring chip to enable the periodic task information, the aperiodic task information and the load statistical information in the operation period to form node operation information of the operation period.
Based on another aspect of the embodiments of the present invention, a system for selecting an auxiliary edge node by an operation monitoring chip is provided, including:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method according to any of the embodiments.
According to a further aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing a computer program for executing the method of any one of the above embodiments.
Based on still another aspect of the embodiments of the present invention, there is provided an electronic device, including: a processor and a memory; wherein, the first and the second end of the pipe are connected with each other,
the memory to store the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method according to any of the embodiments.
According to a further aspect of the embodiments of the present invention, there is provided a computer program product including computer readable code, which when run on a device, a processor in the device executes a method for implementing any of the embodiments described above.
According to the method and system for selecting an auxiliary edge node by an operation monitoring chip, the computer-readable storage medium, the electronic device and the computer program product of the embodiments of the invention, when the operation deviation degree of a selected or specific edge node is greater than a deviation degree threshold value, the auxiliary edge node is reselected for the selected or specific edge node by the operation monitoring chip. In this way, the present application is able to update the secondary edge nodes in the associated set of secondary nodes for a selected or particular edge node in a timely manner when a change in the operational state of the selected or particular edge node is detected, e.g., removing an ineligible secondary edge node from the set of secondary nodes and moving an eligible candidate edge node into the set of secondary nodes. Accordingly, the technical scheme of the application ensures that the load probability of the auxiliary edge node is in a lower state when the load of the selected or specific edge node is higher. In this case, if a task that cannot be processed or cannot be processed in time of a selected or specific edge node is sent or migrated to the auxiliary edge node, system resources or computing resources of the auxiliary edge node are fully utilized, so that response time is reduced, and overall processing efficiency of the edge node in the data domain is improved.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow diagram of a method for selecting a secondary edge node by an operation monitoring chip according to an embodiment of the invention;
FIG. 2 is a schematic diagram of data fields according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of updating a secondary edge node according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of task execution according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a system for selecting an auxiliary edge node by an operation monitoring chip according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In addition, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their context in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Fig. 1 is a flowchart of a method for selecting an auxiliary edge node by an operation monitoring chip according to an embodiment of the present invention. As shown in fig. 1, the method begins at step 101.
Step 101, when the current operation cycle is finished, the operation monitoring chip of the first edge node determines the operation deviation degree of the first edge node between the current operation cycle and the previous operation cycle based on the node operation information of the current operation cycle and the node operation information of the previous operation cycle.
According to an embodiment, before step 101, or before determining the operation deviation degree of the first edge node between the current operation cycle and the previous operation cycle, further comprises, for each operation cycle (for example, the operation cycle includes the current operation cycle and the previous operation cycle), performing:
the operation monitoring chip generates a periodic task record for each periodic task of the first edge node, wherein the periodic task record comprises: the method comprises the steps of recording a plurality of periodic tasks to form periodic task information, wherein the periodic task information comprises a task identifier, a running period sequence number, task starting time, task ending time and task weight; the operation monitoring chip generates an aperiodic task record for each aperiodic task in the first edge node, wherein the aperiodic task record comprises: and recording a plurality of non-periodic tasks to form non-periodic task information. The operation cycle is, for example, 1 natural day, 7 natural days, or any reasonable time length. For example, the current operating cycle is 0 minutes 0 seconds at 6 months and 6 days 0 at 2022 to 59 minutes 59 seconds at 6 months and 6 days 23 at 2022. The last operating cycle is 0 minutes and 0 seconds at 6 months and 5 days of 2022 to 59 minutes and 59 seconds at 23 months and 5 days of 2022. The periodic task is, for example, a task that is repeatedly executed in each operation cycle (for example, the current operation cycle and the last operation cycle). For example, the periodic task may start to be executed at a fixed time in each operating cycle, and determine the ending execution time in different cycles according to the actual execution situation. In addition, the periodic task can be executed at different times in each running period, and the execution time of finishing in different periods is determined according to the actual execution situation. An aperiodic task is, for example, a task that occurs randomly in any operating period or is specified to occur in any operating period.
And the operation monitoring chip forms load statistical information by the load record of the first edge node in each time unit of the operation period. The load records include, for example: real-time processor usage, average processor usage, real-time memory idle, and average memory idle. Wherein the real-time processor usage per time unit refers to the processor usage per acquisition point, acquisition interval, or acquisition granularity per time unit, and the average of the processor usage for all acquisition points per time unit is taken as the average processor usage per time unit. The real-time memory idle rate in each time unit refers to the memory idle rate of each acquisition point in each time unit, and the average value of the memory idle rates of all acquisition points in each time unit is taken as the average memory idle rate in each time unit. Such as an acquisition point, an acquisition interval, or an acquisition granularity of 1 second, 2 seconds, 5 seconds, etc.
And the operation monitoring chip forms the node operation information of the operation period by the periodic task information, the aperiodic task information and the load statistical information in the operation period. The node operation information is stored in a storage area preset in a memory of the first edge node, and the load record comprises the real-time processor utilization rate in each time unit and the average processor utilization rate in each time unit.
According to an embodiment, the method for determining the operation deviation degree of the first edge node between the current operation period and the previous operation period by the operation monitoring chip of the first edge node based on the node operation information of the current operation period and the node operation information of the previous operation period comprises the following steps:
the operation monitoring chip of the first edge node acquires periodic task information, aperiodic task information and load statistical information of the current operation period from the node operation information of the current operation period; the operation monitoring chip acquires node operation information of a previous operation period (for example, in a storage area preset in a memory of the first edge node), and acquires periodic task information, aperiodic task information and load statistical information of the previous operation period from the node operation information of the previous operation period. Determining a first deviation degree based on the periodic task information of the previous operation period and the periodic task information of the current operation period; determining a second deviation degree based on the aperiodic task information of the previous operation period and the aperiodic task information of the current operation period; determining a third deviation degree based on the load statistical information of the previous operation period and the load statistical information of the current operation period; and determining the operation deviation degree of the first edge node between the current operation period and the last operation period based on the first deviation degree, the second deviation degree and the third deviation degree.
According to an embodiment, wherein determining the first degree of deviation based on the periodic task information of the previous operation cycle and the periodic task information of the current operation cycle comprises: acquiring a plurality of periodic task records of the previous operating period from the periodic task information of the previous operating period; acquiring a plurality of periodic task records of the current operation period from the periodic task information of the current operation period; and forming a periodic task record pair by the two periodic task records with the same task identifier in the previous operating period and the current operating period based on the operating period sequence number. For example, the task record for the last run cycle is: the task flag is c5, the run cycle number is the number of the previous run cycle (for example, 20220605), the task start time is 9 minutes 15 seconds at 11 days 6 months 5 days 2022 years, 42 minutes 50 seconds at 13 days 5 months 6 months 5 days 2022 years, and the task weight is 0.05. The task records of the current operating cycle are: the task flag is c5, the operation cycle number is the number of the current operation cycle (for example, 20220606), the task start time is 19 minutes 36 seconds at 11 days 6 months 2022, the task end time is 06 minutes 23 seconds at 14 days 6 months 2022, and the task weight is 0.05. Therefore, the two periodic tasks with the same task identification are the same periodic task but run in different running periods, so that the running period sequence numbers of the two periodic tasks with the same task identification are different. Two periodic tasks with the same task identification and located in adjacent running periods form a periodic task pair, and periodic task records of the two periodic tasks form a periodic task record pair.
In one embodiment, based on each periodic task record pair, determining a periodic time difference value ratio between a last operation period and a current operation period of the same periodic task with the same task identification; a first degree of deviation is determined based on the periodic time difference ratio and the task weight. Wherein, based on each periodic task record pair, determining a periodic time difference value ratio between a last operation period and a current operation period of the same periodic task with the same task identification comprises:
extracting the PBT (task start time) of the same periodic task in the last operating period from each periodic task record pair i And task end time PET i And a task start time CBT within the current run period i And task end time CET i ；
Determining a periodic time difference value ratio between the last operating period and the current operating period of the same periodic task with the same task identification based on the following formula:
Figure 420987DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 786109DEST_PATH_IMAGE022
for the periodic time difference ratio between the last operating cycle and the current operating cycle of the ith periodic task, PBT i Starting time of the ith periodic task in the last operating cycle; PET i The task ending time of the ith periodic task in the last operating period is defined; CBT i The task starting time of the ith periodic task in the current operation period is set; CET (CET) i The task ending time of the ith periodic task in the current operation period;
Figure 205589DEST_PATH_IMAGE023
i and np are natural numbers and np is the number of periodic tasks. In this example, PBT i 、PET i 、CBT i And CET i Has a value such that
Figure 353673DEST_PATH_IMAGE024
。
In one embodiment, determining the first degree of departure based on the periodic time difference value ratio and the task weight includes:
determining a task weight of each periodic task based on a plurality of periodic task records of a current operation period;
calculating a first degree of deviation based on the following formula:
Figure 50365DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 461755DEST_PATH_IMAGE026
in order to be the first degree of deviation,
Figure 227586DEST_PATH_IMAGE027
the task weight for the ith periodic task,
Figure 117044DEST_PATH_IMAGE028
the periodic time difference ratio between the last operating period and the current operating period is set for the ith periodic task. In an embodiment, the number of periodic tasks of the previous running period and the current running period is the same, and the task identification of each periodic task of the previous running period and the current running period is the same. That is, the same periodic task was run in the previous run cycle and the current run cycle. In the current operation period, the current time period,
Figure 933822DEST_PATH_IMAGE029
=1, i.e. the sum of the task weights of all periodic tasks in the current run cycle (likewise, the last run cycle) is 1.
In one embodiment, determining the second deviation degree based on the aperiodic task information of the previous operation period and the aperiodic task information of the current operation period includes:
acquiring a plurality of aperiodic task records of the previous operating period from the aperiodic task information of the previous operating period, and determining the number of aperiodic tasks in the previous operating period and a first aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the previous operating period;
acquiring a plurality of aperiodic task records of the current operating period from the aperiodic task information of the current operating period, and determining the number of aperiodic tasks in the current operating period and a second aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the current operating period;
determining a quantity threshold value based on the quantity of the non-periodic tasks and the first non-periodic tasks in the last operation period, and the quantity of the non-periodic tasks and the second non-periodic tasks in the current operation period;
determining at least one high concurrent operation time interval of the previous operation cycle based on the plurality of aperiodic task records and the quantity threshold of the previous operation cycle, and determining at least one high concurrent operation time interval of the current operation cycle based on the plurality of aperiodic task records and the quantity threshold of the current operation cycle;
determining a second degree of deviation based on the at least one high concurrent runtime interval of the last operating cycle and the at least one high concurrent runtime interval of the current operating cycle.
In one embodiment, wherein determining the number of non-periodic tasks in the last operating cycle and the first non-periodic task with the longest task processing time based on the plurality of non-periodic task records of the last operating cycle comprises:
and determining the quantity of the non-periodic task records in the last operation period as the quantity of the non-periodic tasks in the last operation period. It should be appreciated that in the last run period, non-periodic tasks with the same task sequence number are not included, and thus, the number of non-periodic task records may be considered as the number of non-periodic tasks in the last run period.
And determining the task processing time of each non-periodic task based on the last running period based on the absolute value of the difference value between the task starting time and the task ending time in each non-periodic task record of the last running period. For example, if the start time of the aperiodic task C6 is 2022 years, 6 months, 5 days, 11 hours, 0 minutes and 0 seconds, and the end time is 2022 years, 6 months, 5 days, 12 hours, 0 minutes and 0 seconds, the task processing time is the absolute value of the difference between 12 hours at 6 months, 5 days, 2022 years, 6 months, 5 days, 11 hours at 2022 years, that is, 1 hour or 60 minutes.
In one embodiment, the aperiodic task with the longest task processing time in the last operating cycle is determined as the first aperiodic task.
In one embodiment, wherein determining the number of aperiodic tasks and the second aperiodic task with longest task processing time in the current operating cycle based on a plurality of aperiodic task records of the current operating cycle comprises:
determining the quantity of the aperiodic task records of the current operation period as the quantity of the aperiodic tasks in the current operation period; it should be appreciated that in the current operating cycle, aperiodic tasks with the same task sequence number are not included, and thus, the number of aperiodic task records of the current operating cycle can be considered as the number of aperiodic tasks in the current operating cycle.
And determining the task processing time of each non-periodic task based on the current operation period based on the absolute value of the difference value between the task starting time and the task ending time in each non-periodic task record of the current operation period. For example, if the start time of the aperiodic task C5 is 2022 years, 6 months, 6 days, 11 hours, 0 minutes and 0 seconds, and the end time is 2022 years, 6 months, 6 days, 13 hours, 0 minutes and 0 seconds, the task processing time is an absolute value of a difference between 2022 years, 6 months, 6 days, 13 hours, and 2022 years, 6 months, 6 days, 11 hours, or 120 minutes. And determining the aperiodic task with the longest task processing time in the current operation period as a second aperiodic task.
In one embodiment, determining the number threshold based on the number of aperiodic tasks and the first aperiodic task in the last operating cycle and the number of aperiodic tasks and the second aperiodic tasks in the current operating cycle comprises:
acquiring a task processing time Tt1 of a first aperiodic task and acquiring a task processing time Tt2 of a second aperiodic task;
acquiring the time length Tq1 of the previous operation period and the time length Tq2 of the current operation period;
the quantity threshold is determined based on the following formula:
Figure 312850DEST_PATH_IMAGE030
wherein TH is a number threshold, tt1 is a task processing time of the first aperiodic task, tt2 is a task processing time of the second aperiodic task, tq1 is a time length of a previous operation period, tq2 is a time length of a current operation period, tn1 is a number of aperiodic tasks in the previous operation period, tn2 is a number of aperiodic tasks in the current operation period,
Figure 706923DEST_PATH_IMAGE031
in order to adjust the factor for the first time,
Figure 524706DEST_PATH_IMAGE032
for the purpose of adjusting the factor for the second time,
Figure 55044DEST_PATH_IMAGE033
，
Figure 418024DEST_PATH_IMAGE034
，
Figure 299392DEST_PATH_IMAGE035
。
in one embodiment, wherein determining at least one high concurrency runtime interval for a previous run cycle based on a number of aperiodic task records and a number threshold for the previous run cycle comprises: based on the task start time and the task end time in the aperiodic task records of the previous operation cycle, a task concurrent operation graph of the aperiodic tasks of the previous operation cycle is generated, as shown in fig. 4. Fig. 4 is a schematic diagram of task execution, namely a schematic diagram of task concurrent operation according to an embodiment of the present invention. In the task concurrent operation graph of the last operation period, at least one high concurrent operation time interval in which the number of the non-periodic tasks which are concurrently operated is greater than or equal to the number threshold is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
In fig. 4, a task concurrent execution diagram or a schematic diagram of task execution of a current operation cycle or a previous operation cycle is shown. Where the start is the start time of the current or previous operating cycle, e.g., 0 minutes 0 seconds at 0 on 6 months and 6 days in 2022, and the end is the end time of the current or previous operating cycle, e.g., 59 minutes 59 seconds at 23 on 6 months and 6 days in 2022. Wherein the periodic tasks include: periodic tasks c1, c2, c3, c4, c5, c6, c7, and c8; the aperiodic task includes: aperiodic tasks a1, a2, a3, a4, a5, a6, a7, a8, and a9.
As shown in fig. 4, the concurrent runtime interval includes: f1, f2, f3 and f4, wherein the number of non-periodic tasks concurrently running in the concurrent running time intervals f1, f2 and f4 (f 1 is 3, f2 is 4 and f4 is 3) is greater than or equal to 3, so that f1, f2 and f4 are high concurrent running time intervals; and the number (2) of non-periodic tasks concurrently running of the concurrent running time interval f3 is less than 3, so f3 is a non-high concurrent running time interval.
In one embodiment, wherein determining at least one high concurrency runtime interval for the current run cycle based on the plurality of aperiodic task records and the quantity threshold for the current run cycle comprises: generating a task concurrent running graph of a plurality of aperiodic tasks of the current running period based on the task starting time and the task ending time in a plurality of aperiodic task records of the current running period, as shown in fig. 4); in the task concurrent operation graph of the current operation cycle, at least one high concurrent operation time interval with the quantity of the non-periodic tasks which are operated concurrently being larger than or equal to a quantity threshold value is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
In one embodiment, wherein determining the second degree of deviation based on the at least one high concurrent runtime interval of the last run cycle and the at least one high concurrent runtime interval of the current run cycle comprises:
selecting a high concurrent operation time interval with the maximum time length of the previous operation period from at least one high concurrent operation time interval of the previous operation period as a first operation interval, and determining the starting time FB1 and the ending time FE1 of the first operation interval;
selecting a high concurrent operation time interval with the maximum time length of the current operation time period from at least one high concurrent operation time interval of the current operation period as a second operation interval, and determining the start time FB2 and the end time FE2 of the second operation interval;
the second deviation degree when there is no overlapping time between the first operation interval and the second operation interval
Figure 592970DEST_PATH_IMAGE036
Is 1;
when the first operation interval and the second operation interval have the overlapping time, determining the length TO of the overlapping time between the first operation interval and the second operation interval;
at that time
Figure 368028DEST_PATH_IMAGE037
Second degree of deviation
Figure 26543DEST_PATH_IMAGE036
Is 0.5;
at that time
Figure 97722DEST_PATH_IMAGE038
Second degree of deviation
Figure 867095DEST_PATH_IMAGE039
。
And FB1 is the starting time of the first operation interval, FE1 is the ending time of the first operation interval, FB2 is the starting time of the second operation interval, FE2 is the ending time of the second operation interval, and TO is the overlapping time length between the first operation interval and the second operation interval.
In one embodiment, wherein determining the third deviation degree based on the load statistics of the last operation cycle and the load statistics of the current operation cycle comprises:
determining a load record in each time unit of the previous operation period according to the load statistical information of the previous operation period, determining an average processor utilization rate in each time unit according to the previous operation period according to the load record in each time unit of the previous operation period, and determining the first time unit number Nt1 of the time units of which the average processor utilization rate is greater than a utilization rate threshold value on the basis of the average processor utilization rate in each time unit of the previous operation period;
determining a load record in each time unit of the current operation period according to the load statistical information of the current operation period, determining an average processor utilization rate of each time unit according to the current operation period according to the load record in each time unit of the current operation period, and determining a second time unit number Nt2 of the time units of which the average processor utilization rate is greater than a utilization rate threshold value based on the average processor utilization rate of each time unit of the current operation period;
determining the number N1 of time units of the last operation period and the number N2 of time units of the current operation period; and
the third degree of deviation is determined based on the first number of time units Nt1, the number of time units N1 of the previous operation cycle, the second number of time units Nt2, and the number of time units N2 of the current operation cycle.
In one embodiment, wherein determining the third degree of deviation based on the first number of time units Nt1, the number of time units of the previous operation cycle N1, the second number of time units Nt2, and the number of time units of the current operation cycle N2 comprises:
determining a third degree of deviation based on the following equation
Figure 762238DEST_PATH_IMAGE040
：
Figure 591654DEST_PATH_IMAGE041
Figure 244352DEST_PATH_IMAGE042
Wherein Nt1 is the number of first time units, N1 is the number of time units of the last operation cycle, nt2 is the number of second time units, N2 is the number of time units of the current operation cycle,
Figure 958361DEST_PATH_IMAGE043
To get
Figure 317799DEST_PATH_IMAGE044
And
Figure 708329DEST_PATH_IMAGE045
the minimum value of (d);
wherein
Figure 520427DEST_PATH_IMAGE046
，
Figure 38127DEST_PATH_IMAGE047
Nt1 is the first time unit number of time units with average processor utilization greater than the utilization threshold, nt2 is the second time unit number of time units with average processor utilization greater than the utilization threshold, N1 is the number of time units of the previous operating cycle, and N2 is the number of time units of the current operating cycle.
In one embodiment, wherein determining the degree of deviation of the operation of the first edge node between the current operation cycle and the previous operation cycle based on the first degree of deviation, the second degree of deviation, and the third degree of deviation comprises:
based on the following formulaCalculating the degree of deviation of operation
Figure 517650DEST_PATH_IMAGE048
：
Figure 751185DEST_PATH_IMAGE049
Wherein the content of the first and second substances,
Figure 440792DEST_PATH_IMAGE050
in order to be the first degree of deviation,
Figure 621238DEST_PATH_IMAGE051
in order to be the second degree of deviation,
Figure 565054DEST_PATH_IMAGE040
in order to be the third degree of deviation,
Figure 172753DEST_PATH_IMAGE052
is a first adjustment factor for the first frequency of the frequency band,
Figure 349657DEST_PATH_IMAGE053
is a second adjustment factor to be used for the second adjustment factor,
Figure 864952DEST_PATH_IMAGE054
is the third adjustment factor. Wherein, the first and the second end of the pipe are connected with each other,
Figure 319067DEST_PATH_IMAGE055
and is and
Figure 970103DEST_PATH_IMAGE056
，
Figure 509669DEST_PATH_IMAGE057
and is
Figure 890972DEST_PATH_IMAGE058
。
Step 102, when the running deviation degree is greater than the deviation degree threshold value, determining a first task matching degree of the first edge node and each auxiliary edge node in the auxiliary node set associated with the first edge node. Preferably, the deviation threshold is any reasonable value such as 5%, 10%, 12%, 15%, 20%, and 25%.
In one embodiment, wherein determining a first task match for the first edge node and each secondary edge node in the set of secondary nodes associated with the first edge node comprises:
acquiring load statistical information of the current operation period from the node operation information of the current operation period of the first edge node, acquiring a load record in each time unit of the current operation period of the first edge node according to the load statistical information of the current operation period,
acquiring load statistical information of a current operation period of each auxiliary edge node in an auxiliary node set, and acquiring a load record in each time unit of the current operation period of each auxiliary edge node based on the load statistical information of the current operation period of each auxiliary edge node;
and determining a first task matching degree of the first edge node and each auxiliary edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each auxiliary edge node.
In one embodiment, wherein determining a first task matching degree of the first edge node and each auxiliary edge node based on the load record in each time unit of the current operation cycle of the first edge node and the load record in each time unit of the current operation cycle of each auxiliary edge node comprises:
determining an average processor utilization rate in each time unit of a current operating cycle of the first edge node based on the load record in each time unit of the current operating cycle of the first edge node;
determining an average processor utilization rate in each time unit of the current operating cycle of each secondary edge node based on the load record in each time unit of the current operating cycle of each secondary edge node;
for a first edge node, determining a number NCU of time units for which an average processor usage by the first edge node is greater than a first usage threshold over a current run period;
for each secondary edge node, determining a number NCS of time units for which the average processor usage by each secondary edge node during the current run period is less than a second usage threshold j ；
Wherein the second usage threshold is less than the first usage threshold;
when the jth auxiliary edge node satisfies
Figure 199593DEST_PATH_IMAGE059
Then, the jth auxiliary edge node is matched with the first task matching degree Ma of the first edge node j Is set to 1;
when the jth auxiliary edge node satisfies
Figure 24461DEST_PATH_IMAGE060
Then, a first task matching degree Ma of the jth auxiliary edge node and the first edge node is calculated based on the following formula j ：
Figure 582481DEST_PATH_IMAGE061
When the jth auxiliary edge node satisfies
Figure 908421DEST_PATH_IMAGE062
Then, a first task matching degree Ma of the jth auxiliary edge node and the first edge node is calculated based on the following formula j ：
Figure 196182DEST_PATH_IMAGE063
Wherein the content of the first and second substances,
Figure 316585DEST_PATH_IMAGE064
j and Nnode are natural numbers, and Nnode is in the auxiliary node setThe number of secondary edge nodes.
In step 103, the edge nodes which are not the auxiliary edge nodes in all (other) edge nodes belonging to the same data domain as the first edge node except the first edge node are selected as candidate edge nodes.
In one embodiment, the method further comprises determining an associated secondary node set for the first edge node in the same data domain in advance, each edge node in the associated secondary node set being a secondary edge node of the first edge node, as shown in fig. 2. Fig. 2 is a schematic diagram of a data field according to an embodiment of the present invention. In an edge computing system, a plurality of data fields are included. Each secondary edge node associated with the first edge node is included in the set of secondary nodes associated with the first edge node. Edge nodes outside the set of secondary nodes associated with the first edge node may be considered candidate edge nodes. It should be appreciated that the set of auxiliary nodes, auxiliary edge nodes, and candidate edge nodes are logical divisions or sets. Therefore, each edge node in the data domain has a respective auxiliary edge node and candidate edge nodes, and the auxiliary node set is composed of a plurality of auxiliary edge nodes. Thus, for any edge node (including the first edge node), the edge nodes in the data domain are logically divided into: an edge node (itself), a plurality of auxiliary edge nodes for the edge node, and a plurality of candidate edge nodes for the edge node.
Selecting as candidate edge nodes that are not auxiliary edge nodes among all (other) edge nodes belonging to the same data domain as the first edge node, including: and selecting edge nodes which are not auxiliary edge nodes in the auxiliary node set from all the edge nodes except the first edge node belonging to the same data domain as the first edge node as candidate edge nodes of the first edge node.
And 104, determining a dynamic matching degree threshold value based on the first task matching degree of the first edge node and each auxiliary edge node, and removing the auxiliary edge nodes of which the first task matching degree is smaller than the dynamic matching degree threshold value from the auxiliary node set.
In one embodiment, determining the dynamic threshold of degree of match based on the first task degree of match of the first edge node and each auxiliary edge node comprises:
determining an average of a plurality of first task matches based on the first task matches of the first edge node and each auxiliary edge node
Figure 706109DEST_PATH_IMAGE065
And a minimum value Mina of the first task match degrees;
calculating a dynamic matching degree threshold value Mh based on the following formula:
Figure 304581DEST_PATH_IMAGE066
wherein Mh is a dynamic matching degree threshold value,
Figure 446849DEST_PATH_IMAGE065
the average value of the first task matching degrees is Mina, and the minimum value of the first task matching degrees is Mina.
And removing the auxiliary edge nodes with the first task matching degree smaller than the dynamic matching degree threshold Mh from the auxiliary node set, as shown in fig. 3. Fig. 3 is a diagram illustrating updating an auxiliary edge node according to an embodiment of the present invention.
Step 105, calculating a second task matching degree of each candidate edge node and the first edge node, selecting at least one auxiliary edge node from the plurality of candidate edge nodes based on the second task matching degree, and adding the selected at least one auxiliary edge node to the auxiliary node set of the first edge node.
In one embodiment, wherein calculating the second task matching degree of each candidate edge node with the first edge node comprises:
acquiring load statistical information of the current operation period from the node operation information of the current operation period of the first edge node, acquiring a load record in each time unit of the current operation period of the first edge node according to the load statistical information of the current operation period,
acquiring load statistical information of the current operation period of each candidate edge node to acquire a load record in each time unit of the current operation period of each candidate edge node based on the load statistical information of the current operation period of each candidate edge node;
and determining a second task matching degree of the first edge node and each candidate edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each candidate edge node.
In one embodiment, wherein determining the second task matching degree of the first edge node and each candidate edge node based on the load record in each time unit of the current operation cycle of the first edge node and the load record in each time unit of the current operation cycle of each candidate edge node comprises:
determining an average processor utilization rate in each time unit of the current operating cycle of the first edge node based on the load record in each time unit of the current operating cycle of the first edge node;
determining an average processor utilization rate in each time unit of the current operating cycle of each candidate edge node based on the load record in each time unit of the current operating cycle of each candidate edge node;
for a first edge node, determining a number NDU of time units for which an average processor utilization of the first edge node is greater than a first utilization threshold in a current run period;
for each candidate edge node, determining a number NDS of time units for which an average processor usage of each candidate edge node during a current run period is less than a second usage threshold j ；
Wherein the second usage threshold is less than the first usage threshold;
when the jth candidate edge node satisfies
Figure 3733DEST_PATH_IMAGE067
Then, the second task matching degree Md of the jth candidate edge node and the first edge node is set j Is set to 1;
when the jth candidate edge node satisfies
Figure 614974DEST_PATH_IMAGE068
Then, a second task matching degree Md of the jth candidate edge node and the first edge node is calculated based on the following formula j ：
Figure 282715DEST_PATH_IMAGE069
When the jth candidate edge node satisfies
Figure 951594DEST_PATH_IMAGE070
Then, a second task matching degree Md of the jth candidate edge node and the first edge node is calculated based on the following formula j ：
Figure 804012DEST_PATH_IMAGE071
Wherein the content of the first and second substances,
Figure 27183DEST_PATH_IMAGE072
j and Nnode are natural numbers, and Nnode is the number of auxiliary edge nodes in the auxiliary node set.
In one embodiment, wherein selecting at least one secondary edge node from the plurality of candidate edge nodes based on the second task matching degree comprises: and selecting the candidate edge node with the second task matching degree larger than the static matching degree threshold value as the auxiliary edge node, as shown in fig. 3.
In one embodiment, when a first edge node determines that a newly received aperiodic task needs to be scheduled, a second edge node is selected from a plurality of secondary edge nodes in an associated set of secondary nodes; the newly received aperiodic task is sent to the second edge node so that the newly received aperiodic task is processed by the second edge node.
Fig. 5 is a schematic structural diagram of a system for selecting an auxiliary edge node by an operation monitoring chip according to an embodiment of the present invention. As shown in fig. 5, the system includes:
a first determining device 501, configured to, at the end of the current operating period, cause the operation monitoring chip of the first edge node to determine, based on the node operation information of the current operating period and the node operation information of the previous operating period, an operation deviation degree of the first edge node between the current operating period and the previous operating period.
In an embodiment, the first determining device 501 is specifically configured to cause the operation monitoring chip of the first edge node to obtain periodic task information, aperiodic task information, and load statistics information of the current operation cycle from the node operation information of the current operation cycle;
prompting an operation monitoring chip (in a preset storage area in a memory) to acquire the node operation information of the previous operation period, and acquiring the periodic task information, the aperiodic task information and the load statistical information of the previous operation period from the node operation information of the previous operation period;
determining a first deviation degree based on the periodic task information of the last operation period and the periodic task information of the current operation period;
determining a second deviation degree based on the aperiodic task information of the previous operation period and the aperiodic task information of the current operation period;
determining a third deviation degree based on the load statistical information of the previous operation period and the load statistical information of the current operation period;
and determining the operation deviation degree of the first edge node between the current operation period and the last operation period based on the first deviation degree, the second deviation degree and the third deviation degree.
In one embodiment, the first determining device 501 is specifically configured to obtain multiple periodic task records of a previous operating cycle from periodic task information of the previous operating cycle;
acquiring a plurality of periodic task records of the current operation period from the periodic task information of the current operation period;
forming a periodic task record pair by two periodic task records with the same task identifier in the previous operating period and the current operating period based on the operating period sequence number;
determining a periodic time difference value ratio between the last operating period and the current operating period of the same periodic task with the same task identification based on each periodic task record pair;
a first degree of deviation is determined based on the periodic time difference ratio and the task weight.
In an embodiment, the first determining means 501 is specifically configured to extract, from each periodic task record pair, a task start time PBT of the same periodic task in the last operating cycle i And task end time PET i And a task start time CBT within the current run period i And task end time CET i ；
Determining a periodic time difference value ratio between the last operating period and the current operating period of the same periodic task with the same task identification based on the following formula:
Figure 845753DEST_PATH_IMAGE073
wherein, the first and the second end of the pipe are connected with each other,
Figure 837980DEST_PATH_IMAGE002
for the periodic time difference ratio between the last operating cycle and the current operating cycle of the ith periodic task, PBT i Starting time of the ith periodic task in the last operating cycle; PET i The task ending time of the ith periodic task in the last operating period is defined; CBT i The task starting time of the ith periodic task in the current operation period is set; CET (CET) i When the task in the current operation period is finished for the ith periodic taskA (c) is added;
Figure 861300DEST_PATH_IMAGE074
i and np are natural numbers and np is the number of periodic tasks.
In one embodiment, the first determining device 501 is specifically configured to determine a task weight of each periodic task based on a plurality of periodic task records of a current operating cycle;
calculating a first degree of deviation based on the following formula:
Figure 306188DEST_PATH_IMAGE075
wherein the content of the first and second substances,
Figure 112470DEST_PATH_IMAGE076
in order to be the first degree of deviation,
Figure 100148DEST_PATH_IMAGE077
the task weight for the ith periodic task,
Figure 904156DEST_PATH_IMAGE078
the periodic time difference ratio between the last operating period and the current operating period is set for the ith periodic task.
In one embodiment, the first determining device 501 is specifically configured to obtain a plurality of aperiodic task records of a previous operation cycle from the aperiodic task information of the previous operation cycle, and determine the number of aperiodic tasks in the previous operation cycle and a first aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the previous operation cycle;
acquiring a plurality of aperiodic task records of the current operating period from the aperiodic task information of the current operating period, and determining the number of aperiodic tasks in the current operating period and a second aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the current operating period;
determining a quantity threshold value based on the quantity of the non-periodic tasks and the first non-periodic tasks in the last operation period, and the quantity of the non-periodic tasks and the second non-periodic tasks in the current operation period;
determining at least one high-concurrency operation time interval of the last operation cycle based on the plurality of aperiodic task records and the number threshold of the last operation cycle, and determining at least one high-concurrency operation time interval of the current operation cycle based on the plurality of aperiodic task records and the number threshold of the current operation cycle;
determining a second degree of deviation based on the at least one high concurrent runtime interval of the last operating cycle and the at least one high concurrent runtime interval of the current operating cycle.
In an embodiment, the first determining device 501 is specifically configured to determine the number of aperiodic task records in the previous operating cycle as the number of aperiodic tasks in the previous operating cycle;
determining the task processing time of each non-periodic task based on the previous operating cycle based on the absolute value of the difference value between the task starting time and the task ending time in each non-periodic task record of the previous operating cycle;
and determining the aperiodic task with the longest task processing time in the last operating period as the first aperiodic task.
In an embodiment, the first determining device 501 is specifically configured to determine the number of aperiodic task records in the current operating period as the number of aperiodic tasks in the current operating period;
determining the task processing time of each non-periodic task based on the current operation period based on the absolute value of the difference value between the task starting time and the task ending time in each non-periodic task record of the current operation period;
and determining the aperiodic task with the longest task processing time in the current operation period as a second aperiodic task.
In one embodiment, the first determining device 501 is specifically configured to obtain a task processing time Tt1 of a first aperiodic task, and obtain a task processing time Tt2 of a second aperiodic task;
acquiring the time length Tq1 of the previous operation period and acquiring the time length Tq2 of the current operation period;
the quantity threshold is determined based on the following formula:
Figure 226553DEST_PATH_IMAGE079
wherein TH is a number threshold, tt1 is a task processing time of a first aperiodic task, tt2 is a task processing time of a second aperiodic task, tq1 is a time length of a previous operation cycle, tq2 is a time length of a current operation cycle, tn1 is a number of aperiodic tasks in the previous operation cycle, tn2 is a number of aperiodic tasks in the current operation cycle,
Figure 508630DEST_PATH_IMAGE080
in order to adjust the factor for the first time,
Figure 350815DEST_PATH_IMAGE081
the factor is adjusted for a second time.
In an embodiment, the first determining device 501 is specifically configured to generate a task concurrent execution graph of a plurality of aperiodic tasks in a previous operation cycle based on task start times and task end times in a plurality of aperiodic task records in the previous operation cycle;
in the task concurrent operation graph of the last operation period, at least one high concurrent operation time interval in which the number of the non-periodic tasks which are concurrently operated is greater than or equal to the number threshold is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
In one embodiment, the first determining device 501 is specifically configured to generate a task concurrent running graph of a plurality of aperiodic tasks of the current running period based on the task start time and the task end time in the plurality of aperiodic task records of the current running period, see fig. 5;
in the task concurrent operation graph of the current operation cycle, at least one high concurrent operation time interval with the quantity of the non-periodic tasks which are operated concurrently being larger than or equal to a quantity threshold value is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
In an embodiment, the first determining device 501 is specifically configured to select, from at least one high concurrent operation time interval of a previous operation cycle, a high concurrent operation time interval with a largest time length of the previous operation cycle as a first operation interval, and determine a start time FB1 and an end time FE1 of the first operation interval;
selecting a high concurrent operation time interval with the maximum time length of the current operation time period from at least one high concurrent operation time interval of the current operation period as a second operation interval, and determining the starting time FB2 and the ending time FE2 of the second operation interval;
a second deviation degree when there is no overlapping time between the first operation section and the second operation section
Figure 325724DEST_PATH_IMAGE082
Is 1;
when the first operation interval and the second operation interval have the overlapping time, determining the length TO of the overlapping time between the first operation interval and the second operation interval;
when in use
Figure 135418DEST_PATH_IMAGE083
Second degree of deviation
Figure 486764DEST_PATH_IMAGE082
Is 0.5;
when in use
Figure 104828DEST_PATH_IMAGE084
Second degree of deviation
Figure 391584DEST_PATH_IMAGE085
。
In an embodiment, the first determining device 501 is specifically configured to determine a load record in each time unit of a previous operating cycle according to the load statistical information of the previous operating cycle, determine an average processor usage rate in each time unit according to the previous operating cycle according to the load record in each time unit of the previous operating cycle, determine, based on the average processor usage rate in each time unit of the previous operating cycle, a first time unit number Nt1 of time units in which the average processor usage rate is greater than a usage rate threshold;
determining a load record in each time unit of the current operation period according to the load statistical information of the current operation period, determining an average processor utilization rate of each time unit according to the current operation period according to the load record in each time unit of the current operation period, and determining a second time unit number Nt2 of the time units of which the average processor utilization rate is greater than a utilization rate threshold value based on the average processor utilization rate of each time unit of the current operation period;
determining the number N1 of time units of the previous operation period and the number N2 of time units of the current operation period; and
the third degree of deviation is determined based on the first number of time units Nt1, the number of time units N1 of the previous operation cycle, the second number of time units Nt2, and the number of time units N2 of the current operation cycle.
In one embodiment, the first determining means 501 is specifically adapted to determine the third degree of deviation based on the following formula
Figure 298360DEST_PATH_IMAGE086
：
Figure 46873DEST_PATH_IMAGE087
Figure 988284DEST_PATH_IMAGE088
Wherein Nt1 is the number of first time units, N1 is the number of time units of the last operation cycle, nt2 is the number of second time units, N2 is the number of time units of the current operation cycle,
Figure 177432DEST_PATH_IMAGE089
To get
Figure 368242DEST_PATH_IMAGE090
And
Figure 61392DEST_PATH_IMAGE091
minimum value of (d);
wherein
Figure 981943DEST_PATH_IMAGE092
，
Figure 469556DEST_PATH_IMAGE093
。
In one embodiment, the first determining means 501 is specifically configured to calculate the degree of operational deviation based on the following formula
Figure 226291DEST_PATH_IMAGE094
：
Figure 457552DEST_PATH_IMAGE095
Wherein, the first and the second end of the pipe are connected with each other,
Figure 232610DEST_PATH_IMAGE096
in order to be the first degree of deviation,
Figure 953442DEST_PATH_IMAGE097
in order to be the second degree of deviation,
Figure 322106DEST_PATH_IMAGE098
in order to be the third degree of deviation,
Figure 232424DEST_PATH_IMAGE099
in order to be the first adjustment factor,
Figure 471776DEST_PATH_IMAGE100
is a second adjustment factor to be used for the second adjustment factor,
Figure 691404DEST_PATH_IMAGE101
is the third adjustment factor.
Second determining means 502 for causing a determination of a first task match degree for the first edge node and each auxiliary edge node in the set of auxiliary nodes associated with the first edge node when the running deviation degree is greater than the deviation degree threshold. The deviation thresholds are 5%, 10%, 12%, 15%, 20% and 25%.
In one embodiment, the second determining means 502 is configured to cause the load statistics information of the current operating cycle to be obtained from the node operating information of the current operating cycle of the first edge node, obtain the load record in each time unit of the current operating cycle of the first edge node according to the load statistics information of the current operating cycle,
acquiring load statistical information of a current operation period of each auxiliary edge node in an auxiliary node set, and acquiring a load record in each time unit of the current operation period of each auxiliary edge node based on the load statistical information of the current operation period of each auxiliary edge node;
and determining a first task matching degree of the first edge node and each auxiliary edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each auxiliary edge node.
In one embodiment, the second determining means 502 is configured to determine the average processor usage rate per time unit of the current operating cycle of the first edge node based on the load record per time unit of the current operating cycle of the first edge node;
determining an average processor utilization rate in each time unit of a current operation period of each auxiliary edge node based on a load record in each time unit of the current operation period of each auxiliary edge node;
for a first edge node, determining a number NCU of time units for which an average processor usage by the first edge node is greater than a first usage threshold over a current run period;
for each oneSecondary edge nodes determining the number of time units NCS for which the average processor usage by each secondary edge node during the current run period is less than a second usage threshold j ；
Wherein the second usage threshold is less than the first usage threshold;
when the jth auxiliary edge node satisfies
Figure 547365DEST_PATH_IMAGE102
Then, the jth auxiliary edge node is matched with the first task matching degree Ma of the first edge node j Is set to 1;
when the jth auxiliary edge node satisfies
Figure 917166DEST_PATH_IMAGE103
Then, a first task matching degree Ma of the jth auxiliary edge node and the first edge node is calculated based on the following formula j ：
Figure 417549DEST_PATH_IMAGE104
When the jth auxiliary edge node satisfies
Figure 683445DEST_PATH_IMAGE105
Then, a first task matching degree Ma of the jth auxiliary edge node and the first edge node is calculated based on the following formula j ：
Figure 620177DEST_PATH_IMAGE106
Wherein the content of the first and second substances,
Figure 262511DEST_PATH_IMAGE107
j and Nnode are natural numbers, and Nnode is the number of auxiliary edge nodes in the auxiliary node set.
Selecting means 503 for selecting, as the candidate edge node, an edge node that is not the auxiliary edge node among all (other) edge nodes except the first edge node belonging to the same data domain as the first edge node. In one embodiment, the method further comprises determining an associated set of secondary nodes for the first edge node in the same data domain in advance, each edge node in the associated set of secondary nodes being a secondary edge node of the first edge node.
In one embodiment, the selecting means 503 is specifically configured to select, as the candidate edge node of the first edge node, an edge node that is not an auxiliary edge node in the auxiliary node set, from all edge nodes except the first edge node belonging to the same data domain as the first edge node.
A removing device 504, configured to determine a dynamic matching degree threshold based on the first task matching degree of the first edge node and each auxiliary edge node, and remove the auxiliary edge nodes whose first task matching degree is smaller than the dynamic matching degree threshold from the auxiliary node set.
In one embodiment, the removing means 504 is specifically configured to determine an average value of the plurality of first task matching degrees based on the first task matching degrees of the first edge node and each auxiliary edge node
Figure 538772DEST_PATH_IMAGE108
And a minimum value Mina of the first task match degrees;
calculating a dynamic matching degree threshold Mh based on the following formula:
Figure 842147DEST_PATH_IMAGE109
an adding device 505, configured to calculate a second task matching degree between each candidate edge node and the first edge node, select at least one secondary edge node from the multiple candidate edge nodes based on the second task matching degree, and add the selected at least one secondary edge node to the secondary node set of the first edge node.
In one embodiment, the adding device 505 is specifically configured to obtain the load statistics information of the current operating cycle from the node operating information of the current operating cycle of the first edge node, obtain the load record in each time unit of the current operating cycle of the first edge node according to the load statistics information of the current operating cycle,
acquiring load statistical information of the current operation period of each candidate edge node to acquire a load record in each time unit of the current operation period of each candidate edge node based on the load statistical information of the current operation period of each candidate edge node;
and determining a second task matching degree of the first edge node and each candidate edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each candidate edge node.
In one embodiment, the adding means 505 is specifically configured to determine the average processor utilization rate in each time unit of the current operating cycle of the first edge node based on the load record in each time unit of the current operating cycle of the first edge node;
determining an average processor utilization rate in each time unit of the current operating cycle of each candidate edge node based on the load record in each time unit of the current operating cycle of each candidate edge node;
for a first edge node, determining a number NDU of time units for which an average processor utilization of the first edge node is greater than a first utilization threshold in a current run period;
for each candidate edge node, determining a number NDS of time units for which an average processor usage for each candidate edge node in a current run cycle is less than a second usage threshold j ；
Wherein the second usage threshold is less than the first usage threshold;
when the jth candidate edge node satisfies
Figure 407120DEST_PATH_IMAGE110
Then, the second task matching degree Md of the jth candidate edge node and the first edge node is set j Is set to 1;
when the jth candidate edge node satisfies
Figure 446620DEST_PATH_IMAGE111
Then, a second task matching degree Md of the jth candidate edge node and the first edge node is calculated based on the following formula j ：
Figure 780650DEST_PATH_IMAGE112
When the jth candidate edge node satisfies
Figure 263715DEST_PATH_IMAGE113
Then, a second task matching degree Md of the jth candidate edge node and the first edge node is calculated based on the following formula j ：
Figure 315984DEST_PATH_IMAGE114
Wherein the content of the first and second substances,
Figure 831279DEST_PATH_IMAGE115
j and Nnode are natural numbers, and Nnode is the number of auxiliary edge nodes in the auxiliary node set.
In one embodiment, the adding device 505 is specifically configured to select a candidate edge node with the second task matching degree greater than the static matching degree threshold as the secondary edge node.
Selecting a second edge node from a plurality of auxiliary edge nodes in the associated set of auxiliary nodes when the first edge node determines that a newly received aperiodic task needs to be scheduled; the newly received aperiodic task is sent to the second edge node so that the newly received aperiodic task is processed by the second edge node.
Recording means 506 for, for each operating cycle: the method comprises the following steps of prompting an operation monitoring chip to generate a periodic task record for each periodic task of a first edge node, wherein the periodic task record comprises: the method comprises the steps of recording a plurality of periodic tasks to form periodic task information, wherein the periodic task information comprises a task identifier, a running period sequence number, task starting time, task ending time and task weight; prompting the operation monitoring chip to generate an aperiodic task record for each aperiodic task in the first edge node, wherein the aperiodic task record comprises: recording a plurality of non-periodic tasks to form non-periodic task information; prompting the operation monitoring chip to form load statistical information by the load record of the first edge node in each time unit of the operation period; and prompting the operation monitoring chip to enable the periodic task information, the aperiodic task information and the load statistical information in the operation period to form node operation information of the operation period.
The node operation information is stored in a storage area preset in a memory of the first edge node, and the load record comprises the real-time processor utilization rate in each time unit and the average processor utilization rate in each time unit.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the ones disclosed above are equally possible within the scope of these appended patent claims, as these are known to those skilled in the art.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a// the [ device, component, etc ]" are to be interpreted openly as at least one instance of a device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

Claims (19)

1. A method of selecting a secondary edge node by an operational monitoring chip, the method comprising:
when the current operation period is finished, the operation monitoring chip of the first edge node determines the operation deviation degree of the first edge node between the current operation period and the previous operation period based on the node operation information of the current operation period and the node operation information of the previous operation period;
determining a first task match score for a first edge node and each auxiliary edge node in a set of auxiliary nodes associated with the first edge node when the running deviation score is greater than a deviation score threshold;
selecting edge nodes which are not auxiliary edge nodes from all edge nodes except the first edge node and belong to the same data domain as the first edge node as candidate edge nodes;
determining a dynamic matching degree threshold value based on the first task matching degree of the first edge node and each auxiliary edge node, and removing the auxiliary edge nodes of which the first task matching degree is smaller than the dynamic matching degree threshold value from the auxiliary node set; and
calculating a second task matching degree of each candidate edge node and the first edge node, selecting at least one auxiliary edge node from the candidate edge nodes based on the second task matching degree, and adding the selected at least one auxiliary edge node to an auxiliary node set of the first edge node;
further comprising, for each operating cycle:
the operation monitoring chip generates a periodic task record for each periodic task of the first edge node, wherein the periodic task record comprises: the method comprises the steps of recording a plurality of periodic tasks to form periodic task information, wherein the periodic task information comprises a task identifier, a running period sequence number, task starting time, task ending time and task weight;
the operation monitoring chip generates an aperiodic task record for each aperiodic task in the first edge node, wherein the aperiodic task record comprises: recording a plurality of aperiodic tasks to form aperiodic task information;
the operation monitoring chip enables load records of the first edge node in each time unit of an operation period to form load statistical information; and
and the operation monitoring chip forms the node operation information of the operation period by the periodic task information, the aperiodic task information and the load statistical information in the operation period.
2. The method of claim 1, wherein the determining, by the operation monitoring chip of the first edge node, the operation deviation degree of the first edge node between the current operation cycle and the previous operation cycle based on the node operation information of the current operation cycle and the node operation information of the previous operation cycle comprises:
the operation monitoring chip of the first edge node acquires periodic task information, aperiodic task information and load statistical information of the current operation period from the node operation information of the current operation period;
the operation monitoring chip acquires node operation information of a previous operation period, and acquires periodic task information, aperiodic task information and load statistical information of the previous operation period from the node operation information of the previous operation period;
determining a first deviation degree based on the periodic task information of the previous operation period and the periodic task information of the current operation period;
determining a second deviation degree based on the aperiodic task information of the previous operation period and the aperiodic task information of the current operation period;
determining a third deviation degree based on the load statistical information of the previous operation period and the load statistical information of the current operation period;
and determining the operation deviation degree of the first edge node between the current operation period and the last operation period based on the first deviation degree, the second deviation degree and the third deviation degree.
3. The method of claim 2, wherein determining the first degree of deviation based on the periodic task information of the previous operation cycle and the periodic task information of the current operation cycle comprises:
acquiring a plurality of periodic task records of the previous operating period from the periodic task information of the previous operating period;
acquiring a plurality of periodic task records of the current operation period from the periodic task information of the current operation period;
forming a periodic task record pair by two periodic task records with the same task identifier in the previous operating period and the current operating period based on the operating period sequence number;
determining a periodic time difference value ratio between the last operating period and the current operating period of the same periodic task with the same task identification based on each periodic task record pair;
a first degree of deviation is determined based on the periodic time difference ratio and the task weight.
4. The method of claim 3, wherein determining a periodic time difference ratio between a last operating cycle and a current operating cycle for the same periodic task with the same task identification based on each periodic task record pair comprises:
extracting the task starting time and the task ending time of the same periodic task in the last operating period and the task starting time and the task ending time in the current operating period from each periodic task record pair;
determining a periodic time difference value ratio between the last operating period and the current operating period of the same periodic task with the same task identification based on the following formula:
Figure 941385DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 719985DEST_PATH_IMAGE002
for the periodic time difference ratio between the last operating cycle and the current operating cycle of the ith periodic task, PBT i The task starting time of the ith periodic task in the last operating period is set; PET i Is the ith periodicityThe task ending time of the task in the last operation period; CBT i The task starting time of the ith periodic task in the current operation period is set; CET (CET) i The task ending time of the ith periodic task in the current operation period;
Figure 384185DEST_PATH_IMAGE003
i and np are natural numbers, np is the number of periodic tasks.
5. The method of claim 4, wherein determining the first degree of departure based on the periodic time difference value ratio and the mission weight comprises:
determining a task weight of each periodic task based on a plurality of periodic task records of a current operation period;
determining a first degree of deviation based on the following equation:
Figure 906433DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 90421DEST_PATH_IMAGE005
in order to be the first degree of deviation,
Figure 836660DEST_PATH_IMAGE006
is the task weight of the ith periodic task.
6. The method of claim 2, determining the second degree of deviation based on the aperiodic task information of the previous operation period and the aperiodic task information of the current operation period, comprising:
acquiring a plurality of aperiodic task records of the previous operating period from the aperiodic task information of the previous operating period, and determining the number of aperiodic tasks in the previous operating period and a first aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the previous operating period;
acquiring a plurality of aperiodic task records of the current operating period from the aperiodic task information of the current operating period, and determining the number of aperiodic tasks in the current operating period and a second aperiodic task with the longest task processing time based on the plurality of aperiodic task records of the current operating period;
determining a quantity threshold value based on the quantity of the non-periodic tasks and the first non-periodic tasks in the last operation period, and the quantity of the non-periodic tasks and the second non-periodic tasks in the current operation period;
determining at least one high concurrent operating time interval of the previous operating cycle based on the plurality of aperiodic task records of the previous operating cycle and the quantity threshold, and determining at least one high concurrent operating time interval of the current operating cycle based on the plurality of aperiodic task records of the current operating cycle and the quantity threshold;
determining a second degree of deviation based on the at least one high concurrent runtime interval of the last operating cycle and the at least one high concurrent runtime interval of the current operating cycle.
7. The method of claim 6, wherein determining at least one high concurrency runtime interval for a last run cycle based on a plurality of aperiodic task records for the last run cycle and the quantity threshold comprises:
generating a task concurrent running graph of a plurality of non-periodic tasks of the previous running period based on the task starting time and the task ending time in the plurality of non-periodic task records of the previous running period;
in the task concurrent operation graph of the last operation period, at least one high concurrent operation time interval in which the number of the non-periodic tasks which are concurrently operated is greater than or equal to the number threshold is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
8. The method of claim 6, wherein determining at least one high concurrency runtime interval for a current operating cycle based on a plurality of aperiodic task records for the current operating cycle and the quantity threshold comprises:
generating a task concurrent operation diagram of a plurality of aperiodic tasks in the current operation period based on task start time and task end time in a plurality of aperiodic task records in the current operation period;
in the task concurrent operation graph of the current operation cycle, at least one high concurrent operation time interval with the quantity of the non-periodic tasks which are operated concurrently being larger than or equal to the quantity threshold value is determined, and the starting time and the ending time of each high concurrent operation time interval are determined.
9. The method of claim 2, wherein determining the third degree of deviation based on the load statistics of the previous operating cycle and the load statistics of the current operating cycle comprises:
determining a load record in each time unit of the previous operation period according to the load statistical information of the previous operation period, determining an average processor utilization rate in each time unit according to the previous operation period according to the load record in each time unit of the previous operation period, and determining the first time unit number of the time units of which the average processor utilization rate is greater than a utilization rate threshold value on the basis of the average processor utilization rate in each time unit of the previous operation period;
determining a load record in each time unit of the current operation period according to the load statistical information of the current operation period, determining an average processor utilization rate of each time unit according to the current operation period according to the load record in each time unit of the current operation period, and determining the number of second time units of the time units of which the average processor utilization rate is greater than a utilization rate threshold value based on the average processor utilization rate of each time unit of the current operation period;
determining the number of time units of the previous operation period and the number of time units of the current operation period; and
and determining a third deviation degree based on the number of the first time units, the number of the time units of the previous operation period, the number of the second time units and the number of the time units of the current operation period.
10. The method of claim 2, wherein determining a degree of operational deviation of the first edge node between a current operational cycle and a previous operational cycle based on the first degree of deviation, the second degree of deviation, and the third degree of deviation comprises:
determining a degree of operational deviation based on the following formula
Figure 597942DEST_PATH_IMAGE007
：
Figure 48515DEST_PATH_IMAGE008
Wherein the content of the first and second substances,
Figure 946064DEST_PATH_IMAGE009
is a first degree of deviation of the first degree of deviation,
Figure 933044DEST_PATH_IMAGE010
in order to provide the second degree of deviation,
Figure 447202DEST_PATH_IMAGE011
in order to be the third degree of deviation,
Figure 435887DEST_PATH_IMAGE012
in order to be the first adjustment factor,
Figure 250259DEST_PATH_IMAGE013
is a second adjustment factor to be used for the second adjustment factor,
Figure 807142DEST_PATH_IMAGE014
is a third adjustment factor;
wherein the content of the first and second substances,
Figure 418383DEST_PATH_IMAGE015
，
Figure 554966DEST_PATH_IMAGE016
，
Figure 817321DEST_PATH_IMAGE017
，
Figure 545105DEST_PATH_IMAGE018
。
11. the method of claim 1, wherein determining a first task match score for a first edge node and each secondary edge node in a set of secondary nodes associated with the first edge node comprises:
acquiring load statistical information of the current operation period from the node operation information of the current operation period of the first edge node, acquiring a load record in each time unit of the current operation period of the first edge node according to the load statistical information of the current operation period,
acquiring load statistical information of a current operation period of each auxiliary edge node in an auxiliary node set, and acquiring a load record in each time unit of the current operation period of each auxiliary edge node based on the load statistical information of the current operation period of each auxiliary edge node;
and determining a first task matching degree of the first edge node and each auxiliary edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each auxiliary edge node.
12. The method of claim 1, further comprising determining an associated set of secondary nodes in the same data domain for the first edge node in advance, each edge node in the associated set of secondary nodes being a secondary edge node of the first edge node.
13. The method of claim 1, wherein selecting edge nodes that are not secondary edge nodes among all edge nodes except the first edge node belonging to the same data domain as the first edge node as candidate edge nodes comprises:
and selecting edge nodes which are not auxiliary edge nodes in the auxiliary node set from all the edge nodes except the first edge node belonging to the same data domain as the first edge node as candidate edge nodes of the first edge node.
14. The method of claim 11, wherein determining a dynamic match threshold based on the first task match of the first edge node and each secondary edge node comprises:
determining an average value of the plurality of first task matching degrees and a minimum value of the plurality of first task matching degrees based on the first task matching degrees of the first edge node and each auxiliary edge node;
determining a dynamic matching degree threshold value based on the following formula:
Figure 378063DEST_PATH_IMAGE019
wherein Mh is a dynamic matching degree threshold value,
Figure 849496DEST_PATH_IMAGE020
is the average value of the first task matching degrees, and Mina is the minimum value of the first task matching degrees.
15. The method of claim 1, wherein calculating a second task match metric for each candidate edge node with the first edge node comprises:
acquiring load statistical information of the current operation period from the node operation information of the current operation period of the first edge node, acquiring a load record in each time unit of the current operation period of the first edge node according to the load statistical information of the current operation period,
acquiring load statistical information of the current operation period of each candidate edge node to acquire a load record in each time unit of the current operation period of each candidate edge node based on the load statistical information of the current operation period of each candidate edge node;
and determining a second task matching degree of the first edge node and each candidate edge node based on the load record in each time unit of the current operation period of the first edge node and the load record in each time unit of the current operation period of each candidate edge node.
16. The method of claim 1, wherein selecting at least one secondary edge node from a plurality of candidate edge nodes based on a second degree of task matching comprises:
and selecting the candidate edge nodes with the second task matching degree larger than the static matching degree threshold value as auxiliary edge nodes.
17. The method of claim 1, further comprising, when the first edge node determines that a newly received aperiodic task needs to be scheduled, selecting a second edge node from a plurality of secondary edge nodes in an associated set of secondary nodes;
sending the newly received aperiodic task to the second edge node, whereby the newly received aperiodic task is processed by the second edge node.
18. A system for selecting a secondary edge node by an operation monitoring chip, the system comprising:
the first determining device is used for prompting the operation monitoring chip of the first edge node to determine the operation deviation degree of the first edge node between the current operation period and the previous operation period based on the node operation information of the current operation period and the node operation information of the previous operation period when the current operation period is finished;
second determining means for causing a determination of a first task match for a first edge node and each secondary edge node in a set of secondary nodes associated with the first edge node when the running deviation is greater than a deviation threshold;
selecting means for selecting, as candidate edge nodes, edge nodes that are not auxiliary edge nodes among all edge nodes except the first edge node belonging to the same data domain as the first edge node;
removing means for determining a dynamic matching degree threshold based on the first task matching degree of the first edge node and each auxiliary edge node, and removing the auxiliary edge nodes of which the first task matching degree is smaller than the dynamic matching degree threshold from the auxiliary node set; and
the adding device is used for calculating a second task matching degree of each candidate edge node and the first edge node, selecting at least one auxiliary edge node from the candidate edge nodes based on the second task matching degree, and adding the selected at least one auxiliary edge node into an auxiliary node set of the first edge node;
recording means for, for each operating cycle: the method comprises the following steps of prompting an operation monitoring chip to generate a periodic task record for each periodic task of a first edge node, wherein the periodic task record comprises: the method comprises the steps that task identification, a running period sequence number, task starting time, task ending time and task weight are carried out, and a plurality of periodic task records form periodic task information; prompting the operation monitoring chip to generate an aperiodic task record for each aperiodic task in the first edge node, wherein the aperiodic task record comprises: recording a plurality of non-periodic tasks to form non-periodic task information; prompting the operation monitoring chip to record the load of the first edge node in each time unit of the operation period to form load statistical information; and prompting the operation monitoring chip to enable the periodic task information, the aperiodic task information and the load statistical information in the operation period to form node operation information of the operation period.
19. A system for selecting a secondary edge node by an operation monitoring chip, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method of any one of claims 1-17.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7266540B2 (en) * 2004-03-04 2007-09-04 International Business Machines Corporation Mechanism for dynamic workload rebalancing in a multi-nodal computer system
CN106844027A (en) * 2017-01-13 2017-06-13 广西电网有限责任公司电力科学研究院 A kind of method for scheduling task based on node load
CN113181658A (en) * 2021-05-20 2021-07-30 腾讯科技（深圳）有限公司 Edge computing node scheduling method, device, equipment and medium
CN114077485A (en) * 2021-11-09 2022-02-22 深圳供电局有限公司 Service scheduling deployment method for Internet of things edge computing node resources
CN115080215A (en) * 2022-08-22 2022-09-20 中诚华隆计算机技术有限公司 Method and system for performing task scheduling among computing nodes by state monitoring chip

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180198855A1 (en) * 2014-11-24 2018-07-12 Alibaba Group Holding Limited Method and apparatus for scheduling calculation tasks among clusters

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7266540B2 (en) * 2004-03-04 2007-09-04 International Business Machines Corporation Mechanism for dynamic workload rebalancing in a multi-nodal computer system
CN106844027A (en) * 2017-01-13 2017-06-13 广西电网有限责任公司电力科学研究院 A kind of method for scheduling task based on node load
CN113181658A (en) * 2021-05-20 2021-07-30 腾讯科技（深圳）有限公司 Edge computing node scheduling method, device, equipment and medium
CN114077485A (en) * 2021-11-09 2022-02-22 深圳供电局有限公司 Service scheduling deployment method for Internet of things edge computing node resources
CN115080215A (en) * 2022-08-22 2022-09-20 中诚华隆计算机技术有限公司 Method and system for performing task scheduling among computing nodes by state monitoring chip

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