CN111400045A - Load balancing method and device - Google Patents

Load balancing method and device Download PDF

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Publication number
CN111400045A
CN111400045A CN202010183135.1A CN202010183135A CN111400045A CN 111400045 A CN111400045 A CN 111400045A CN 202010183135 A CN202010183135 A CN 202010183135A CN 111400045 A CN111400045 A CN 111400045A
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load node
key event
event data
internet
load
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CN111400045B (en
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徐立峰
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Hangzhou Hikvision System Technology Co Ltd
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Hangzhou Hikvision System Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/5022—Workload threshold
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/508—Monitor
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate

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  • General Physics & Mathematics (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

The embodiment of the application provides a load balancing method and a device, wherein the method comprises the following steps: the method comprises the steps of obtaining first current instantaneous flow information of various key event data from an internet of things device to be accessed, and obtaining second current instantaneous flow information of various key event data processed by a plurality of load nodes; determining a predicted instantaneous value of the multiple resource state data of each load node under the condition that the to-be-accessed Internet of things device accesses each load node according to the fitting relation between the multiple key event data and the multiple resource state data of each load node and the multiple first current instantaneous flow information and the multiple second current instantaneous flow information corresponding to each load node; selecting a target load node from load nodes of which the predicted instantaneous values of the various resource state data are all lower than a preset state threshold; and accessing the to-be-accessed Internet of things equipment to the target load node. By applying the technical scheme provided by the embodiment of the application, the load balance is realized, and the waste of resources is reduced.

Description

Load balancing method and device
Technical Field
The present application relates to the field of internet of things technology, and in particular, to a load balancing method and apparatus.
Background
With the rapid development of big data technology and internet of things technology, more and more internet of things devices are accessed to an internet of things access system. In the face of huge access pressure, a distributed architecture is widely applied to an internet of things system. In a distributed architecture, a load balancing technology is one of the most important links, and the effect of load balancing directly influences the access capability and stable and reliable performance of an internet of things access system.
At present, a load balancing method of an internet of things access system includes: resource state data such as the utilization rate of a Central Processing Unit (CPU), the utilization rate of a memory, the utilization rate of a bandwidth and the like are converted into a comprehensive performance index. The larger the comprehensive performance index of the load node is, the poorer the effect of accessing the load node is, and the smaller the comprehensive performance index of the load node is, the better the effect of accessing the load node is; and the Internet of things equipment is accessed or migrated into a load node with large comprehensive performance indexes.
However, in practical applications, the load nodes with smaller overall performance indexes are not necessarily better choices, and the load nodes with larger overall performance indexes are not necessarily worse choices. For example, a load node has high CPU consumption and abundant space for memory and bandwidth, but the overall performance index of the node is high, and the load node is not necessarily a better choice when a new internet of things device is accessed or migrated, but is a better choice for the internet of things devices with low CPU consumption and high memory or bandwidth consumption. Therefore, the load balancing method in the related art is adopted to perform load balancing processing on the access of the internet of things device, and the problems of uneven load and resource waste still often occur.
Disclosure of Invention
An object of the embodiments of the present application is to provide a load balancing method and apparatus, so as to implement load balancing and reduce waste of resources. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a load balancing method, where the method includes:
the method comprises the steps of obtaining first current instant flow information of various key event data from an internet of things device to be accessed, and obtaining second current instant flow information of a plurality of load nodes for processing the various key event data;
determining a predicted instantaneous value of the various resource state data of each load node under the condition that the to-be-accessed Internet of things device is accessed to each load node according to a fitting relation between the various key event data and various resource state data of each load node and a plurality of first current instantaneous flow information and a plurality of second current flow information corresponding to each load node;
selecting a target load node from load nodes of which the predicted instantaneous values of the various resource state data are all lower than a preset state threshold;
and accessing the to-be-accessed Internet of things equipment to the target load node.
Optionally, before determining the predicted instantaneous value of the plurality of resource state data of each load node in the case that the to-be-accessed internet of things device accesses each load node, the method further includes:
acquiring historical flow information of each load node for processing the multiple types of key event data in a preset time period and historical values of the multiple types of resource state data of each load node;
and fitting the historical values of the multiple kinds of resource state data of each load node and the historical flow information of the multiple kinds of key event data to obtain the fitting relation between the multiple kinds of key event data and the multiple kinds of resource state data of each load node.
Optionally, before acquiring the first current instant traffic information of the multiple kinds of key event data from the internet of things device to be accessed, the method further includes:
acquiring current instantaneous values of the multiple resource state data of a first load node and current instantaneous flow information of the multiple key event data processed by the first load node, wherein the first load node is a load node of target resource state data of which the current instantaneous values are greater than a preset state threshold value in the multiple resource state data;
determining target key event data influencing the target resource state data in the various key event data and target flow information of the target key event data when the target resource state data reaches the preset state threshold according to the fitting relation between the various key event data and the various resource state data of the first load node;
determining flow information to be migrated according to the target flow information and the current instantaneous flow information of the target key event data processed by the first load node;
and determining the equipment to be accessed into the Internet of things from each Internet of things equipment according to the information of the traffic to be migrated and the third current instant traffic information of the plurality of key event data from each Internet of things equipment processed by the first load node.
Optionally, the step of determining, according to the traffic information to be migrated and the third current instantaneous traffic information of the plurality of key event data processed by the first load node from each equipment in an internet of things, the equipment in an internet of things to be accessed from each equipment in an internet of things includes:
selecting a first type of Internet of things equipment which sends the target key event data and does not send other key event data from all the Internet of things equipment from which the plurality of key event data processed by the first load node come;
and if the third current instantaneous flow information of the target key event data from the selected first type of internet-of-things equipment processed by the first load node is greater than or equal to the to-be-migrated flow information, taking the selected first type of internet-of-things equipment as the to-be-accessed internet-of-things equipment.
In a second aspect, an embodiment of the present application provides a load balancing apparatus, where the apparatus includes:
the system comprises a first acquisition unit, a second acquisition unit and a first processing unit, wherein the first acquisition unit is used for acquiring first current instantaneous flow information of various key event data from an internet of things device to be accessed and acquiring second current instantaneous flow information of a plurality of load nodes for processing the various key event data;
a first determining unit, configured to determine, according to a fitting relationship between the multiple types of key event data and multiple types of resource state data of each load node, and multiple pieces of the first current instantaneous traffic information and multiple pieces of the second current instantaneous traffic information corresponding to each load node, a predicted instantaneous value of the multiple types of resource state data of each load node when the to-be-accessed internet-of-things device accesses each load node;
the selection unit is used for selecting a target load node from the load nodes of which the predicted instantaneous values of the various resource state data are lower than a preset state threshold value;
and the access unit is used for accessing the to-be-accessed Internet of things equipment to the target load node.
Optionally, the apparatus further comprises:
a second obtaining unit, configured to obtain historical traffic information of each load node processing the multiple types of key event data within a preset time period and historical values of the multiple types of resource state data of each load node before determining a predicted instantaneous value of the multiple types of resource state data of each load node when the to-be-accessed internet-of-things device accesses each load node;
and the fitting unit is used for fitting the historical values of the multiple types of resource state data of each load node and the historical flow information of the multiple types of key event data to obtain the fitting relation between the multiple types of key event data and the multiple types of resource state data of each load node.
Optionally, the apparatus further comprises:
a third obtaining unit, configured to obtain current instantaneous values of multiple types of resource state data of a first load node before obtaining first current instantaneous flow information of multiple types of key event data from an internet of things device to be accessed, and obtain current instantaneous flow information of the multiple types of key event data processed by the first load node, where the first load node is a load node in the multiple types of resource state data, where target resource state data whose current instantaneous values are greater than a preset state threshold exists;
a second determining unit, configured to determine, according to a fitting relationship between the multiple types of key event data and multiple types of resource state data of the first load node, target key event data that affects the target resource state data in the multiple types of key event data, and target traffic information of the target key event data when the target resource state data reaches the preset state threshold;
a third determining unit, configured to determine to-be-migrated traffic information according to the target traffic information and current instantaneous traffic information of the target key event data processed by the first load node;
a fourth determining unit, configured to determine, according to the traffic information to be migrated and third current instantaneous traffic information of the plurality of key event data from each equipment in the internet of things processed by the first load node, the equipment in the internet of things to be accessed from each equipment in the internet of things.
Optionally, the fourth determining unit is specifically configured to:
selecting a first type of Internet of things equipment which sends the target key event data and does not send other key event data from all the Internet of things equipment from which the plurality of key event data processed by the first load node come;
and if the third current instantaneous flow information of the target key event data from the selected first type of internet-of-things equipment processed by the first load node is greater than or equal to the to-be-migrated flow information, taking the selected first type of internet-of-things equipment as the to-be-accessed internet-of-things equipment.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor and the communication interface complete communication between the memory and the processor through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing any one of the load balancing method steps when executing the program stored in the memory.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method performs any of the load balancing method steps described above.
In a fifth aspect, embodiments of the present application provide a computer program product including instructions, which when run on a computer, cause the computer to perform any of the load balancing method steps described above.
The embodiment of the application has the following beneficial effects:
in the load balancing method and device provided by the embodiment of the application, the fitting relationship between the flow information of various key event data and various resource state data of each load node is preset, and based on the fitting relationship, the influence of which or some key event data each resource state data is mainly influenced can be clearly determined. Therefore, after first current instantaneous flow information of various key event data from the to-be-accessed Internet of things device is acquired, a target load node suitable for the to-be-accessed Internet of things device to access can be accurately determined, wherein the target load node is as follows: after various key event data sent by the internet of things equipment to be accessed are accessed, the predicted instantaneous values of various resource state data are all lower than the load node with the preset state threshold value, resources in the target load node are not wasted, and the waste of the resources is reduced while the load balance is realized.
Of course, not all advantages described above need to be achieved at the same time in the practice of any one product or method of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an internet of things access system provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a load balancing method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a method for determining a fitting relationship between key event data and resource status data according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of a method for determining an internet of things to be accessed according to an embodiment of the present application;
FIG. 5a is a graph of key event data provided by an embodiment of the present application;
FIG. 5b is a graph of resource status data provided in accordance with an embodiment of the present application;
fig. 6 is a schematic structural diagram of a load balancing apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The internet of things access system shown in fig. 1 comprises a monitoring node 11, a management node 12 and load nodes 13-15. The internet of things access system may include a plurality of load nodes, and here, only 3 load nodes are taken as an example for illustration and not for limitation.
In the internet of things access system, the load nodes 13-15 are responsible for carrying and processing data from various internet of things devices. The management node 12 is responsible for managing the access of the equipment of the internet of things to the load nodes 12-15. The monitoring node 11 is responsible for monitoring the operation of the load nodes 13 to 15, and when detecting that the load nodes 13 to 15 are failed or overloaded, informs the management node 12 to migrate the equipment in the internet of things.
In the related art, a load balancing method of an internet of things access system includes: converting resource state data such as the utilization rate of a CPU (Central processing Unit), the utilization rate of a memory, the utilization rate of a bandwidth and the like into a comprehensive performance index; and the Internet of things equipment is accessed or migrated into a load node with large comprehensive performance indexes. However, in practical applications, the load nodes with smaller overall performance indexes are not necessarily better choices, and the load nodes with larger overall performance indexes are not necessarily worse choices. By adopting the load balancing method in the related technology, the access of the internet of things equipment is subjected to load balancing processing, and the problems of uneven load and resource waste still often occur.
In order to solve the above problem, an embodiment of the present application provides a load balancing method applied to an internet of things access system. In the method, fitting relations between various kinds of key event data and various kinds of resource state data of each load node are preset, and based on the fitting relations, the influence of which kind or some key event data each kind of resource state data is mainly influenced can be clearly determined. Therefore, after first current instantaneous flow information of various key event data from the to-be-accessed Internet of things device is acquired, a target load node suitable for the to-be-accessed Internet of things device to access can be accurately determined, wherein the target load node is as follows: after various key event data sent by the internet of things equipment to be accessed are accessed, the predicted instantaneous values of various resource state data are all lower than the load node with the preset state threshold value, resources in the target load node are not wasted, and the waste of the resources is reduced while the load balance is realized.
A load balancing method provided in the embodiments of the present application is described in detail below with specific embodiments.
Referring to fig. 2, fig. 2 is a schematic flowchart of a load balancing method according to an embodiment of the present disclosure. The load balancing method may be applied to the monitoring node 11 as shown in fig. 1. For convenience of description, the following description will use the monitoring node as an execution subject, and is not limited. The load balancing method comprises the following steps.
Step 201, obtaining first current instant traffic information of multiple kinds of key event data from an internet of things device to be accessed, and obtaining second current instant traffic information of multiple load nodes processing multiple kinds of key event data.
In the embodiment of the application, the equipment to be accessed into the internet of things can be the equipment newly accessed into the internet of things access system, and can also be the equipment needing load node migration in the internet of things access system. If the equipment to be accessed into the internet of things is the internet of things equipment newly accessed into the internet of things access system, the plurality of load nodes can be part or all of the load nodes of the internet of things access system. If the equipment to be accessed to the internet of things is the equipment of the internet of things needing to migrate the load node in the internet of things access system, the load nodes can be part or all of the load nodes in the internet of things access system except the load node of the equipment to be accessed to the internet of things needing to migrate.
The key event data is data of events which have large influence on each load node in the Internet of things access system. By way of example, key events include, but are not limited to, sending facial makeup data, sending cloud storage data, sending message queues, maintaining access to an internet of things device, intelligent analysis, and the like. Accordingly, the key event data includes, but is not limited to, face data that needs to be sent to a facial makeup (an analysis server), audio/video data that needs to be sent to a cloud storage, various signaling messages to be processed, data to be analyzed, and the like.
There are various kinds of preset critical event data. And each Internet of things device transmits one or more kinds of preset key event data. For example, there are 3 preset key event data, the internet of things device 1 sends one of the 3 key event data, that is, the current instantaneous flow information of the key event data sent by the internet of things device 1 is not 0, and the current instantaneous flow information of the other two key event data sent by the internet of things device 1 is 0; the internet of things device 2 sends two kinds of key event data in the 3 kinds of key event data, that is, the current instantaneous flow information of the two kinds of key event data sent by the internet of things device 2 is not 0, and the current instantaneous flow information of the other kind of key event data sent by the internet of things device 2 is 0.
Each load node of the Internet of things access system processes key event data from one or more Internet of things devices. Take a kind of critical event data, a load node as an example.
In one embodiment, the load node periodically reports current instantaneous traffic information of the key event data from each internet of things device accessing the load node to the monitoring node, and the current instantaneous traffic information of the key event data processed by the load node. At this time, the monitoring node may periodically obtain the current instantaneous traffic information of the kind of key event data from each internet of things device reported by the load node, and the current instantaneous traffic information of the kind of key event data processed by the load node.
In another embodiment, the load node periodically reports the current instantaneous traffic information of the key event data from each internet-of-things device accessing the load node to the monitoring node. At this time, the monitoring node may periodically obtain the current instantaneous traffic information of the kind of key event data from each internet of things device, which is reported by the load node, and obtain the current instantaneous traffic information of the kind of key event data, which is processed by the load node, based on the current instantaneous traffic information of the kind of key event data from each internet of things device, which is reported by the load node.
If the to-be-accessed internet-of-things device is an internet-of-things device needing to migrate a load node in the internet-of-things access system, the monitoring node can take current instantaneous flow information of the key event data from the internet-of-things device, reported by the load node accessed by the to-be-accessed internet-of-things device, as first current instantaneous flow information; and taking the current instantaneous flow information of the key event data processed by the load nodes reported by a plurality of load nodes in other load nodes as second current instantaneous flow information.
If the equipment to be accessed into the internet of things is the internet of things equipment newly accessed into the internet of things access system, the monitoring node can take the current instantaneous flow information of the key event data processed by the load nodes reported by the plurality of load nodes as second current instantaneous flow information; in addition, the equipment to be accessed into the internet of things can directly report the first current instantaneous flow information of various key event data from the equipment to be accessed into the internet of things to the monitoring node, or the equipment to be accessed into the internet of things can report the first current instantaneous flow information of various key event data from the equipment to be accessed into the internet of things to the monitoring node through the load node.
Step 202, according to the fitting relationship between the multiple kinds of key event data and the multiple kinds of resource state data of each load node, as well as the first current instant traffic information of the multiple kinds of key event data and the second current instant traffic information of each load node processing the multiple kinds of key event data, determining the predicted instant value of the multiple kinds of resource state data of each load node under the condition that the to-be-accessed internet-of-things device is accessed to each load node.
In the embodiment of the present application, the resource status data includes, but is not limited to, a utilization rate of a CPU, a utilization rate of a memory, a utilization rate of a bandwidth, a connection number, and the like. The fitting relation is used for representing a conversion relation between the key event data and the resource state data. The fitting relationship may be preset for a user to quickly determine predicted instantaneous values of the plurality of resource state data. The fitting relationship may also be obtained according to a historical value of resource state data when each load node processes multiple types of key event data and historical traffic information when each load node processes multiple types of key event data when the to-be-accessed internet-of-things device needs to access the internet-of-things access system, which will be described in detail below and will not be described herein again.
In this embodiment, after the monitoring node performs step 201, one piece of first current instantaneous flow information is obtained for one piece of key event data, and multiple pieces of first current instantaneous flow information can be obtained for multiple pieces of key event data. Taking a load node as an example, after the monitoring node performs step 201, one piece of second current instantaneous traffic information is obtained for one piece of key event data, and a plurality of pieces of second current instantaneous traffic information can be obtained for a plurality of pieces of key event data.
And the monitoring node predicts and determines the predicted instantaneous value of the multiple resource state data of each load node under the condition that the to-be-accessed Internet of things equipment is accessed to each load node according to the fitting relation between the multiple key event data and the multiple resource state data of each load node and the multiple first current instantaneous flow information and the multiple second current flow information corresponding to each load node.
For example, the plurality of load nodes includes load nodes 1-3. The resource status data includes CPU usage and bandwidth usage. The key event data includes data a, data B, and data C.
The fitting relationship between the preset various kinds of key event data and the various kinds of resource state data of the load nodes is shown in table 1 below.
TABLE 1
Figure BDA0002413246740000091
In Table 1, PfFlow information, P, representing data AcFlow information, P, representing data BmFlow information, P, representing data CcpuRepresenting CPU usage, PnetRepresenting bandwidth usage.
The monitoring node can determine that the load node 1 processes the total instantaneous flow information P of the data A after the to-be-accessed Internet of things equipment is accessed to the load node 1 based on the plurality of first current instantaneous flow information and the plurality of second current instantaneous flow information corresponding to the load node 1f1The load node 1 processes the total instantaneous flow information P of the data Bc1The load node 1 processes the total instantaneous flow information P of the data Cm1。
The monitoring node can determine that the load node 2 processes the total instantaneous flow information P of the data A after the to-be-accessed internet-of-things equipment is accessed to the load node 2 based on the plurality of first current instantaneous flow information and the plurality of second current instantaneous flow information corresponding to the load node 2f2The load node 2 processes the total instantaneous flow information P of the data Bc2The load node 2 processes the total instantaneous flow information P of the data Cm2。
The monitoring node can determine that the load node 3 processes the total instantaneous flow information P of the data A after the to-be-accessed Internet of things equipment is accessed to the load node 3 based on the plurality of first current instantaneous flow information and the plurality of second current instantaneous flow information corresponding to the load node 3f3The load node 3 processes the total instantaneous flow information P of the data Bc3The load node 3 processes the total instantaneous flow information P of the data Cm3。
Based on Pf1、Pc1And Pm1And the fitting relation in table 1, it can be determined that in the case that the to-be-accessed internet-of-things device accesses the load node 1,a predicted momentary value 11 of the CPU usage and a predicted momentary value 12 of the bandwidth usage of the load node 1.
Based on Pf2、Pc2And Pm2And the fitting relation in table 1, it can be determined that the predicted instantaneous value 21 of the CPU usage rate and the predicted instantaneous value 22 of the bandwidth usage rate of the load node 2 are in the case that the to-be-accessed internet of things device is accessed to the load node 2.
Based on Pf3、Pc3And Pm3And the fitting relation in table 1, it can be determined that the predicted instantaneous value 31 of the CPU usage rate and the predicted instantaneous value 32 of the bandwidth usage rate of the load node 3 are in the case that the to-be-accessed internet of things device accesses the load node 3.
Step 203, selecting a target load node from load nodes of which the predicted instantaneous values of the various resource state data are all lower than a preset state threshold value.
In the embodiment of the application, the preset state threshold value can be set according to actual requirements. For example, the preset state threshold may be 70%, 80%, 85%, 90%, etc.
In one embodiment, for each load node, the monitoring node detects whether the predicted instantaneous values of the plurality of resource state data of the load node are all below a preset state threshold. And if the load nodes are lower than the preset state threshold value, the monitoring node takes the load node as a candidate node. And the monitoring node selects one candidate node from the candidate nodes as a target load node.
In one example, the monitoring node randomly selects one candidate node from the candidate nodes as the target load node.
The example in step 202 is still used as an example for illustration. The monitoring node determines that under the condition that the to-be-accessed Internet of things equipment is accessed to the load nodes 1-3, the predicted instantaneous value 11 of the CPU utilization rate and the predicted instantaneous value 12 of the bandwidth utilization rate of the load node 1, the predicted instantaneous value 21 of the CPU utilization rate and the predicted instantaneous value 22 of the bandwidth utilization rate of the load node 2, the predicted instantaneous value 31 of the CPU utilization rate of the load node 3 and the predicted instantaneous value 32 of the bandwidth utilization rate. If the predicted instantaneous value 11 and the predicted instantaneous value 12 are both smaller than a preset state threshold, the predicted instantaneous value 21 is smaller than the preset state threshold, the predicted instantaneous value 22 is larger than the preset state threshold, and the predicted instantaneous value 31 and the predicted instantaneous value 32 are both smaller than the preset state threshold, the load node 1 and the load node 3 are determined to be candidate nodes. The monitoring node randomly selects a candidate node from the load nodes 1 and 3, and if the load node 1 is selected, the load node 1 is taken as a target load node.
In another example, the monitoring node may calculate the overall performance index of each candidate node, and select a candidate node with the smallest overall performance index from the candidate nodes as the target load node.
In the embodiment of the present application, the monitoring node may also determine the target load node from the candidate nodes in other manners. This is not particularly limited.
And step 204, accessing the equipment to be accessed into the target load node.
The example in step 203 is still used as an example for explanation. The monitoring node selects the load node 1 as a target load node, and the equipment to be accessed into the Internet of things is accessed into the load node 1.
In the embodiment of the application, the monitoring node accurately positions the reason of the load bottleneck based on the fitting relationship between various key event data and various resource state data of each load node, so that the problem that in the related technology, resource state data such as the utilization rate of a CPU (central processing unit), the utilization rate of a memory, the utilization rate of a bandwidth and the like are converted into a comprehensive performance index in a fuzzy mixed mode is solved, invalid and redundant load balancing behaviors are brought, the problems of uneven load and resource waste are solved, load balancing is realized, and resource waste is reduced.
In an embodiment of the application, before determining a predicted instantaneous value of multiple types of resource state data of each load node when the to-be-accessed internet-of-things device accesses each load node, the monitoring node determines a fitting relationship between the multiple types of key event data and the multiple types of resource state data of each load node according to a historical value of the resource state data when each load node processes the multiple types of key event data and historical flow information of the multiple types of key event data processed by each load node. Specifically, referring to the flowchart of the method for determining the fitting relationship between the key event data and the resource status data shown in fig. 3, the method may include the following steps.
Step 301, obtaining historical traffic information of each load node processing multiple types of key event data within a preset time period, and historical values of multiple types of resource state data of each load node.
The preset time period refers to a time period from the current time to the current time, wherein the time period is a preset time length. The preset duration can be set according to actual requirements. The historical flow information may be historical instantaneous flow information and the historical values may be historical instantaneous values. The historical traffic information may be historical average traffic information in a certain period, and the historical value may be a historical average value in a certain period.
For example, the critical event data includes data a, data B, and data C. The resource state data includes CPU usage, memory usage, and bandwidth usage. The historical flow information is historical instantaneous flow information, and the historical value is a historical instantaneous value. The current time is t9, and the preset time period is t1-t 9. Taking a load node as an example, in a time period from t1 to t9, the load node respectively obtains historical instantaneous traffic information 1 of the load node processing data a, historical instantaneous traffic information 2 of the load node processing data B, and historical instantaneous traffic information 3 of the load node processing data C at times t1, t2, t3, t4, t5, t6, t7, and t 8; in addition, the load node obtains historical instantaneous values 1 of the CPU utilization rate of the load node, 2 of the memory utilization rate of the load node and 3 of the bandwidth utilization rate of the load node at times t1, t2, t3, t4, t5, t6, t7 and t8, respectively.
And 302, fitting the historical values of the multiple resource state data of each load node and the historical flow information of the multiple key event data to obtain a fitting relation between the multiple key event data and the multiple resource state data of each load node.
In the embodiment of the application, for each load node, the monitoring node fits the historical values of the multiple resource state data of the load node and the historical traffic information of the multiple key event data to obtain the fitting relation between the multiple key event data and the multiple resource state data of the load node.
In the embodiment of the application, the monitoring node analyzes the historical values of the multiple resource state data of each load node and the historical flow information of the multiple key event data in real time, and updates the fitting relationship between the flow information of the multiple key event data and the multiple resource state data of each load node. The method can ensure that the fitting relation stored in the monitoring node is reasonable and accurate, and further realize load balancing according to the stored fitting relation.
In an embodiment of the application, the to-be-accessed internet-of-things device is an internet-of-things device which needs to migrate a load node in an internet-of-things access system. In this case, before acquiring the first current instantaneous traffic information of the multiple kinds of key event data from the internet of things device to be accessed, the monitoring node needs to determine the internet of things device to be accessed. Specifically, refer to a schematic flow chart of the method for determining the equipment to be accessed through the internet of things shown in fig. 4. The method may include the following steps.
Step 401, obtaining current instantaneous values of the multiple resource state data of the first load node, and current instantaneous traffic information of the multiple key event data processed by the first load node. The first load node is a load node of target resource state data with a current instantaneous value larger than a preset state threshold value in the multiple resource state data.
In this embodiment, the first load node may be any one or more of a plurality of load nodes.
In one embodiment, for each load node, the monitoring node may periodically detect whether a current instantaneous value of each resource status data of the load node is greater than a preset status threshold. If the current instantaneous value of one or more resource state data is larger than the preset state threshold value, the monitoring node determines that the load node is a first load node, the one or more resource state data is target resource state data, the current instantaneous values of the various resource state data of the load node are obtained, and the current instantaneous flow information of various key event data processed by the load node is obtained.
In another embodiment, for each load node, the load node may periodically detect whether a current instantaneous value of each resource status data of the load node is greater than a preset status threshold. If the current instantaneous value of one or more resource state data is larger than the preset state threshold value, the load node is used as a first load node, the current instantaneous values of the multiple resource state data of the load node are reported to the monitoring node, and the current instantaneous flow information of the multiple key event data processed by the load node.
Step 402, according to the fitting relationship between the multiple kinds of key event data and the multiple kinds of resource state data of the first load node, determining target key event data which affects the target resource state data in the multiple kinds of key event data, and target flow information of the target key event data when the target resource state data reaches a preset state threshold.
In the embodiment of the application, the monitoring node determines target key event data influencing target resource state data in the various key event data according to the fitting relation between the various key event data and the various resource state data of the first load node, and determines target flow information of the target key event data when the target resource state data reaches a preset state threshold.
For example, the preset state threshold is 80%. As shown in Table 1, if the target resource status data of the load node 1 is the CPU utilization rate, the fitting relationship based on the load node 1 in Table 1 is adopted
Figure BDA0002413246740000141
The target key event can be determined to be PfAnd the represented flow information of the data A, and further determining the target flow information of the data A when the CPU utilization rate reaches 80%.
In the embodiment of the application, the monitoring node determines the fitting relationship between the multiple kinds of key event data and the multiple kinds of resource state data of each load node in real time according to the historical values of the resource state data when each load node processes the multiple kinds of key event data and the historical flow information of the multiple kinds of key event data processed by each load node. To reduce the amount of computation, the monitoring node may determine only the fitting relationship between the various types of key event data and the target resource status data based on steps 301 and 302.
And step 403, determining to-be-migrated traffic information according to the target traffic information and current instantaneous traffic information of the target key event data processed by the first load node.
In one embodiment, the monitoring node may calculate a difference between the target traffic information and current instantaneous traffic information of the first load node processing the target key event data, so as to obtain the to-be-migrated traffic information. For example, the target traffic information is 200 pieces of traffic information, and the current instantaneous traffic information of the first load node processing the target critical event data is 280 pieces of traffic information, then the monitoring node determines that the to-be-migrated traffic information is 280 pieces of traffic information 200-.
In another embodiment, the monitoring node may calculate a difference between the target traffic information and current instantaneous traffic information of the first load node processing the target critical event data; and calculating the product of the difference value and a preset coefficient to obtain the information of the flow to be migrated. For example, the predetermined coefficient is 1.2. The number of the target flow information is 200, the number of the current instantaneous flow information of the first load node for processing the target key event data is 280, and the monitoring node determines that the number of the flow information to be migrated is (280-200) × 1.2-96.
In this embodiment of the application, the monitoring node may also determine the traffic information to be migrated by using other manners, which is not specifically limited.
Step 404, determining the equipment to be accessed to the internet of things from each internet of things equipment according to the traffic information to be migrated and the third current instantaneous traffic information of the plurality of key event data from each internet of things equipment processed by the first load node.
In one embodiment, among the pieces of internet of things equipment from which the plurality of pieces of key event data processed by the first load node come, that is, among the pieces of internet of things equipment accessed to the first load node, there is a first type of internet of things equipment that transmits the target key event data and does not transmit other key event data. The monitoring node may select a first type of internet-of-things device that transmits the target key event data and does not transmit other key event data from among the various internet-of-things devices from which the plurality of key event data processed by the first load node come. And if the third current instantaneous flow information of the target key event data from the selected first type of internet-of-things equipment processed by the first load node is greater than or equal to the flow information to be migrated, the monitoring node takes the selected first type of internet-of-things equipment as the internet-of-things equipment to be accessed.
In the embodiment of the application, the first-type internet of things device sends the target key event data, but does not send other key event data. The first type of internet of things equipment is migrated from the first load node to other load nodes, so that the first load node can be relieved from overload, and redundant and invalid behaviors of migrating the internet of things equipment are reduced.
In another embodiment, if the third current instantaneous traffic information of the first load node processing the target key event data from the selected first type of internet-of-things device is less than the to-be-migrated traffic information, the monitoring node selects a second type of internet-of-things device that sends the target key event data and sends other key event data. And the monitoring node takes the selected first type of Internet of things equipment and the selected second type of Internet of things equipment as the Internet of things equipment to be accessed. Here, the first type of internet of things device and the second type of internet of things device satisfy the following first condition and second condition. Wherein the first condition is: and the sum of the third current instantaneous flow information of the target key event data sent by the first type of internet of things equipment and the second type of internet of things equipment is greater than or equal to the flow information to be migrated. The second condition is: and in the case that the first condition is met, the sum of the third current instantaneous flow information of other key event data transmitted by the second type of internet of things equipment is minimum.
In the embodiment of the application, when the first type of internet of things device is migrated from the first load node to other load nodes, the second type of internet of things device which sends the target key event data and sends other key event data is selected, so that the first load node can be relieved from overload, and redundant and invalid behaviors of migrating the internet of things device are reduced.
In another embodiment, in each of the pieces of internet of things equipment from which the plurality of pieces of key event data processed by the first load node come, that is, in the piece of internet of things equipment accessed to the first load node, there is no first type of internet of things equipment that sends the target key event data and does not send other pieces of key event data. The monitoring node may select a third type of internet-of-things device that transmits the target key event data and transmits other key event data from among the various internet-of-things devices from which the plurality of key event data processed by the first load node come. And the monitoring node takes the selected third type of Internet of things equipment as the Internet of things equipment to be accessed.
Here, the third type of internet of things satisfies the following third and fourth conditions. Wherein the third condition is: and the third type of internet of things equipment sends the sum of the third current instantaneous flow information of the target key event data to be more than or equal to the flow information to be migrated. The fourth condition is that: and in the case that a third condition is met, the sum of third current instantaneous flow information of other key event data transmitted by the third type of internet of things equipment is minimum.
The following describes the load balancing method provided in the embodiments of the present application in detail with reference to examples. For example, the critical event data includes data a, data B, and data C. The resource state data includes CPU usage, memory usage, and bandwidth usage. The preset state threshold is 80%.
At the time of t9 in step 1, the key event data and the resource state data obtained by the monitoring node to the load node are shown in table 2 below. From table 2, it can be seen that the CPU utilization and bandwidth utilization exceed 80%. That is, CPU usage and bandwidth usage are target resource state data.
TABLE 2
Figure BDA0002413246740000161
Step 2, the monitoring node obtains the key event data and the resource status data of the load nodes from t1 to t8 as shown in the following table 3.
TABLE 3
Figure BDA0002413246740000162
Figure BDA0002413246740000171
A graph of key event data based on key event data from t1-t8 is shown in FIG. 5a, and a graph of resource status data based on resource status data from t1-t8 is shown in FIG. 5 b. In fig. 5a, the abscissa represents time and the ordinate represents flow rate information of the key event data. In fig. 5b, the abscissa represents time and the ordinate represents resource status data.
Step 3, with PcFlow information, P, representing data AfFlow information, P, representing data BmFlow information, P, representing data CcpuRepresenting CPU usage, PnetRepresenting bandwidth usage. The monitoring node was fitted according to the following formula based on the data in table 3.
Figure BDA0002413246740000172
Figure BDA0002413246740000173
Fitting relationships obtained by fitting based on the above formula are as follows.
Figure BDA0002413246740000174
Figure BDA0002413246740000175
At this time, the monitoring node determines that the CPU usage is mainly related to data B, and the bandwidth usage is mainly related to transmitting data a and data B. That is, the target key event includes data a and data B.
And 4, the monitoring node determines that the flow information of the data B is 200 pieces when the CPU utilization rate of the load node reaches 80 percent and the flow information of the data A and the data B is 160 pieces when the bandwidth utilization rate of the load node reaches 80 percent according to the fitting relation obtained in the step 3.
Step 5, the monitoring node analyzes the data at the time t9 (as shown in table 2), and determines that the to-be-migrated traffic information corresponding to the data B is 200-; and for the bandwidth utilization rate, determining that the to-be-migrated traffic information corresponding to the data A and the data B is (20+200) -160-70. At this time, the monitoring node determines that the number of pieces of information of the traffic to be migrated is 70.
Step 6, the monitoring node analyzes the data at the time t9, and obtains current instantaneous flow information of a plurality of key event data from each internet of things device, as shown in table 4.
TABLE 4
Figure BDA0002413246740000176
Figure BDA0002413246740000181
In the above 4 pieces of equipment in the internet of things, the equipment 1 and the equipment 2 only transmit the data a and the data B, the traffic information of the equipment 1 transmitting the data a and the data B is 10+50 ═ 60<70, and the traffic information of the equipment 2 transmitting the data a and the data B is 0+80 ═ 80> 70. Therefore, the monitoring node selects the device 2 as the equipment to be migrated from the internet of things, that is, the equipment to be accessed to another load node.
And 7, predicting the CPU utilization rate, the memory utilization rate and the bandwidth utilization rate of each load node after the equipment 2 is accessed to each load node by the monitoring node according to the steps 2-4. If the CPU utilization, the memory utilization, and the bandwidth utilization of a load node are all less than 80%, the monitoring node accesses the device 2 to the load node.
In the embodiment of the present application, if a plurality of load nodes that can be accessed to the device 2 are determined, then the balance states of the CPU usage rate, the memory usage rate, and the bandwidth usage rate, and/or the balance states of the number of access devices of the plurality of load nodes are comprehensively considered.
Corresponding to the load balancing method, the embodiment of the application also provides a load balancing device. Referring to fig. 6, fig. 6 is a schematic structural diagram of a load balancing apparatus according to an embodiment of the present application, where the apparatus includes: a first obtaining unit 601, a first determining unit 602, a selecting unit 603 and an accessing unit 604.
A first obtaining unit 601, configured to obtain first current instantaneous traffic information of multiple types of key event data from an internet of things device to be accessed, and obtain second current instantaneous traffic information of multiple load nodes processing multiple types of key event data;
a first determining unit 602, configured to determine, according to a fitting relationship between multiple types of key event data and multiple types of resource state data of each load node, and multiple pieces of first current instantaneous traffic information and multiple pieces of second current instantaneous traffic information corresponding to each load node, a predicted instantaneous value of multiple types of resource state data of each load node when the to-be-accessed internet-of-things device accesses each load node;
a selecting unit 603, configured to select a target load node from load nodes whose predicted instantaneous values of the multiple resource state data are all lower than a preset state threshold;
an accessing unit 604, configured to access the to-be-accessed internet of things device to the target load node.
In one embodiment, the load balancing apparatus may further include:
the second obtaining unit is used for obtaining historical flow information of various types of key event data processed by each load node in a preset time period and historical values of various types of resource state data of each load node before determining the predicted instantaneous value of various types of resource state data of each load node under the condition that the to-be-accessed Internet of things equipment is accessed to each load node;
and the fitting unit is used for fitting the historical values of the multiple resource state data of each load node and the historical flow information of the multiple key event data to obtain the fitting relation between the multiple key event data and the multiple resource state data of each load node.
In one embodiment, the load balancing apparatus may further include:
a third obtaining unit, configured to obtain current instantaneous values of multiple resource state data of a first load node and current instantaneous flow information of multiple key event data processed by the first load node before obtaining first current instantaneous flow information of the multiple key event data from an internet of things device to be accessed, where the first load node is a load node in the multiple resource state data, where a target resource state data whose current instantaneous value is greater than a preset state threshold exists;
the second determining unit is used for determining target key event data influencing the target resource state data in the various key event data and target flow information of the target key event data when the target resource state data reaches a preset state threshold according to the fitting relation between the various key event data and the various resource state data of the first load node;
the third determining unit is used for determining the flow information to be migrated according to the target flow information and the current instantaneous flow information of the target key event data processed by the first load node;
and the fourth determining unit is used for determining the equipment to be accessed into the internet of things from each internet of things equipment according to the traffic information to be migrated and the third current instant traffic information of the plurality of key event data from each internet of things equipment processed by the first load node.
In an embodiment, the fourth determining unit may be specifically configured to:
selecting a first type of Internet of things equipment which sends target key event data and does not send other key event data from each Internet of things equipment from which a plurality of key event data processed by a first load node come;
and if the third current instantaneous flow information of the target key event data from the selected first type of internet-of-things equipment processed by the first load node is greater than or equal to the flow information to be migrated, taking the selected first type of internet-of-things equipment as the internet-of-things equipment to be accessed.
In the load balancing device provided in the embodiment of the present application, a fitting relationship between traffic information of multiple types of critical event data and multiple types of resource state data of each load node is preset, and based on the fitting relationship, it can be clearly determined which type or some types of critical event data each type of resource state data is mainly affected by. Therefore, after first current instantaneous flow information of various key event data from the to-be-accessed Internet of things device is acquired, a target load node suitable for the to-be-accessed Internet of things device to access can be accurately determined, wherein the target load node is as follows: after various key event data sent by the internet of things equipment to be accessed are accessed, the predicted instantaneous values of various resource state data are all lower than the load node with the preset state threshold value, resources in the target load node are not wasted, and the waste of the resources is reduced while the load balance is realized.
Corresponding to the above load balancing method, an embodiment of the present application further provides an electronic device, as shown in fig. 7, including a processor 701, a communication interface 702, a memory 703 and a communication bus 704, where the processor 701, the communication interface 702 and the memory 703 complete mutual communication through the communication bus 704,
a memory 703 for storing a computer program;
the processor 701 is configured to implement any step of the load balancing method when executing the program stored in the memory 703.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
Corresponding to the above load balancing method, in another embodiment provided by the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements any step of the above load balancing method.
In accordance with the load balancing method, in another embodiment provided by the present application, there is also provided a computer program product including instructions which, when run on a computer, cause the computer to perform any of the steps of the load balancing method described above.
The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g., from one website site, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DS L)) or wireless (e.g., infrared, wireless, microwave, etc.) manner to another website site, computer, server, or data center.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, the electronic device, the computer-readable storage medium, and the computer program product embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and in relation to them, reference may be made to the partial description of the method embodiments.
The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims (10)

1. A method of load balancing, the method comprising:
the method comprises the steps of obtaining first current instant flow information of various key event data from an internet of things device to be accessed, and obtaining second current instant flow information of a plurality of load nodes for processing the various key event data;
determining a predicted instantaneous value of the various resource state data of each load node under the condition that the to-be-accessed Internet of things device is accessed to each load node according to a fitting relation between the various key event data and various resource state data of each load node and a plurality of first current instantaneous flow information and a plurality of second current flow information corresponding to each load node;
selecting a target load node from load nodes of which the predicted instantaneous values of the various resource state data are all lower than a preset state threshold;
and accessing the to-be-accessed Internet of things equipment to the target load node.
2. The method according to claim 1, wherein prior to determining the predicted instantaneous value of the plurality of resource status data for each load node in the case that the to-be-accessed internet-of-things device accesses each load node, the method further comprises:
acquiring historical flow information of each load node for processing the multiple types of key event data in a preset time period and historical values of the multiple types of resource state data of each load node;
and fitting the historical values of the multiple kinds of resource state data of each load node and the historical flow information of the multiple kinds of key event data to obtain the fitting relation between the multiple kinds of key event data and the multiple kinds of resource state data of each load node.
3. The method of claim 1, wherein prior to obtaining the first current instantaneous traffic information of the plurality of key event data from the internet of things device to be accessed, further comprising:
acquiring current instantaneous values of the multiple resource state data of a first load node and current instantaneous flow information of the multiple key event data processed by the first load node, wherein the first load node is a load node of target resource state data of which the current instantaneous values are greater than a preset state threshold value in the multiple resource state data;
determining target key event data influencing the target resource state data in the various key event data and target flow information of the target key event data when the target resource state data reaches the preset state threshold according to the fitting relation between the various key event data and the various resource state data of the first load node;
determining flow information to be migrated according to the target flow information and the current instantaneous flow information of the target key event data processed by the first load node;
and determining the equipment to be accessed into the Internet of things from each Internet of things equipment according to the information of the traffic to be migrated and the third current instant traffic information of the plurality of key event data from each Internet of things equipment processed by the first load node.
4. The method according to claim 3, wherein the step of determining the to-be-accessed IoT device from each IoT device according to the to-be-migrated traffic information and the third current instantaneous traffic information of the plurality of key event data processed by the first load node from each IoT device comprises:
selecting a first type of Internet of things equipment which sends the target key event data and does not send other key event data from all the Internet of things equipment from which the plurality of key event data processed by the first load node come;
and if the third current instantaneous flow information of the target key event data from the selected first type of internet-of-things equipment processed by the first load node is greater than or equal to the to-be-migrated flow information, taking the selected first type of internet-of-things equipment as the to-be-accessed internet-of-things equipment.
5. A load balancing apparatus, the apparatus comprising:
the system comprises a first acquisition unit, a second acquisition unit and a first processing unit, wherein the first acquisition unit is used for acquiring first current instantaneous flow information of various key event data from an internet of things device to be accessed and acquiring second current instantaneous flow information of a plurality of load nodes for processing the various key event data;
a first determining unit, configured to determine, according to a fitting relationship between the multiple types of key event data and multiple types of resource state data of each load node, and multiple pieces of the first current instantaneous traffic information and multiple pieces of the second current instantaneous traffic information corresponding to each load node, a predicted instantaneous value of the multiple types of resource state data of each load node when the to-be-accessed internet-of-things device accesses each load node;
the selection unit is used for selecting a target load node from the load nodes of which the predicted instantaneous values of the various resource state data are lower than a preset state threshold value;
and the access unit is used for accessing the to-be-accessed Internet of things equipment to the target load node.
6. The apparatus of claim 5, further comprising:
a second obtaining unit, configured to obtain historical traffic information of each load node processing the multiple types of key event data within a preset time period and historical values of the multiple types of resource state data of each load node before determining a predicted instantaneous value of the multiple types of resource state data of each load node when the to-be-accessed internet-of-things device accesses each load node;
and the fitting unit is used for fitting the historical values of the multiple types of resource state data of each load node and the historical flow information of the multiple types of key event data to obtain the fitting relation between the multiple types of key event data and the multiple types of resource state data of each load node.
7. The apparatus of claim 5, further comprising:
a third obtaining unit, configured to obtain current instantaneous values of multiple types of resource state data of a first load node before obtaining first current instantaneous flow information of multiple types of key event data from an internet of things device to be accessed, and obtain current instantaneous flow information of the multiple types of key event data processed by the first load node, where the first load node is a load node in the multiple types of resource state data, where target resource state data whose current instantaneous values are greater than a preset state threshold exists;
a second determining unit, configured to determine, according to a fitting relationship between the multiple types of key event data and multiple types of resource state data of the first load node, target key event data that affects the target resource state data in the multiple types of key event data, and target traffic information of the target key event data when the target resource state data reaches the preset state threshold;
a third determining unit, configured to determine to-be-migrated traffic information according to the target traffic information and current instantaneous traffic information of the target key event data processed by the first load node;
a fourth determining unit, configured to determine, according to the traffic information to be migrated and third current instantaneous traffic information of the plurality of key event data from each equipment in the internet of things processed by the first load node, the equipment in the internet of things to be accessed from each equipment in the internet of things.
8. The apparatus according to claim 7, wherein the fourth determining unit is specifically configured to:
selecting a first type of Internet of things equipment which sends the target key event data and does not send other key event data from all the Internet of things equipment from which the plurality of key event data processed by the first load node come;
and if the third current instantaneous flow information of the target key event data from the selected first type of internet-of-things equipment processed by the first load node is greater than or equal to the to-be-migrated flow information, taking the selected first type of internet-of-things equipment as the to-be-accessed internet-of-things equipment.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 4 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 4.
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