CN115297018B - Operation and maintenance system load prediction method based on active detection - Google Patents

Operation and maintenance system load prediction method based on active detection Download PDF

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CN115297018B
CN115297018B CN202211230766.XA CN202211230766A CN115297018B CN 115297018 B CN115297018 B CN 115297018B CN 202211230766 A CN202211230766 A CN 202211230766A CN 115297018 B CN115297018 B CN 115297018B
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resource
module
allocation
judgment
record
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CN115297018A (en
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刘东海
徐育毅
庞辉富
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Hangzhou Youyun Software Co ltd
Beijing Guangtong Youyun Technology Co ltd
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Hangzhou Youyun Software Co ltd
Beijing Guangtong Youyun Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • 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
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • G06F11/3433—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment for load management
    • 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]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
    • 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]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/865—Monitoring of software
    • 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
    • G06F2209/501—Performance criteria
    • 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
    • G06F2209/5019—Workload prediction
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides an operation and maintenance system load prediction method based on active detection, which comprises the following specific steps: monitoring the resource use condition of the system in real time through a system resource monitoring module, and sending the current resource use condition to a resource prediction module according to a certain time period; the resource demand of the next time period is predicted through the resource prediction module, the active detection module is added, the active detection module calls the allocation judgment module, the allocation judgment module traverses the record list in the allocation record module, the judgment condition is built according to the judgment mark A and the judgment mark B, and if the judgment condition is met, the resource is not reallocated. The invention has the beneficial effects that: the invention provides innovation for an active detection method, analysis is carried out when the resource demand is reduced, if the resource is required to be improved in a short period after the resource is reduced, the resource is not required to be recycled temporarily, and the system stability is kept.

Description

Operation and maintenance system load prediction method based on active detection
Technical Field
The invention relates to the field of intelligent operation and maintenance, in particular to an operation and maintenance system load prediction method based on active detection.
Background
In an operation and maintenance system, workload prediction is very important in relation to the management of later-stage resources. The workload refers to the intensity of tasks that the application service needs to undertake, and mainly refers to how much resource needs to be used to ensure normal operation of the program. The service providing on demand is the core purpose of cloud computing, and in order to provide required computing service capacity for container service efficiently and timely, a system should predict future load workload by identifying a resource usage pattern of a program and adjust the cloud computing capacity owned by a container in advance. Therefore, effective management of the container platform on the memory resources is enhanced through effective and accurate load prediction, service performance can be prevented from being reduced, waste of idle memory resources can be reduced, and profits of enterprises can be further improved.
Load forecasting mainly has the following requirements:
1) Adaptability: the predictive model should be able to adapt to load changes of the application and learn the application dynamic behavior to reduce prediction errors.
2) History data: an effective predictive model should accurately estimate future likely behavior with reference to all effective parameters regarding workload behavior, taking into account correlations between resource patterns discovered from historical sample data.
3) Complexity: in order to predict the load timely and efficiently without affecting the normal operation of the program, the time and space complexity of the prediction model should be well controlled and should not be too complex.
4) Data granularity: the initial stage in designing a predictive model is to determine which resources should be monitored. The length of the sampling interval should then be defined, since coarse-grained long-term sampling would cause the model to lose system dynamics, while the fine-grained of short-term sampling would increase the cost of data collection and processing.
In a resource management system, many allocation schemes can directly allocate resources according to prediction, and when the fluctuation of the resources is large sometimes, the resources are released for a while and recovered, which greatly affects the stability of the system.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an operation and maintenance system load prediction method based on active detection.
The object of the present invention is achieved by the following technical means. An operation and maintenance system load prediction method based on active detection comprises the following specific steps:
(1) Monitoring the resource use condition of the operation and maintenance system in real time through a system resource monitoring module, and sending the current resource use condition to a resource prediction module according to a certain time period;
(2) Predicting the resource demand of the next time period through a resource prediction module, and setting the current resource as Z0 and the predicted resource demand as Z1;
(3) Judging through a resource comparison module, judging whether Z1 is larger than Z0, if so, turning to the step (4), and otherwise, turning to the step (5);
(4) The resource allocation module allocates resources of the next stage according to the predicted resource demand Z1, modifies the allocation recording module, and adds a judgment mark A in the original recording list, wherein the judgment mark A indicates that the resources are increased or unchanged;
(5) Calling an active detection module, calling an allocation judgment module by the active detection module, traversing a record list in the allocation record module by the allocation judgment module, constructing a judgment condition according to a judgment mark A and a judgment mark B, if the judgment condition is met, not reallocating resources, and otherwise, turning to the step (6);
(6) And the resource allocation module allocates the next-stage resource according to the predicted resource demand Z1, modifies the allocation recording module, and adds a judgment mark B in the original recording list, wherein the judgment mark B indicates that the resource is reduced.
Furthermore, the resources comprise a hard disk or a CPU, and when the resources comprise multiple types, each resource is analyzed and predicted respectively.
Further, in the step (4), the method for modifying the allocation record module includes:
assume the original list of records is: d1 D2, d3, … … dm, the modified record list is: d1 D2, d3, … … dm,1; wherein dm represents the record of the m-th resource allocation, the value of which can only be 0 or 1,0 represents that the resource is reduced, and 1 represents that the resource is increased or unchanged.
Further, in the step (5), the allocation determining module traverses the record list in the allocation record module, and if the number of 1's after the first 0's in the record list is greater than the number of 0's after the first 0's, the resource is not reallocated.
Further, in the step (6), the method for modifying the allocation record module includes:
assume the original list of records is: d1 D2, d3, … … dm, the modified record list is: d1 D2, d3, … … dm,0; wherein dm represents the record of the m-th resource allocation, the value of which can only be 0 or 1,0 represents that the resource is reduced, and 1 represents that the resource is increased or unchanged.
The beneficial effects of the invention are as follows: the invention solves the problem that the load prediction method in the prior art is lack of initiative, and the load prediction method in the prior art only judges whether resources are enough according to the existing running condition, but does not have the link of active detection. The invention provides innovation aiming at the active detection method, and ensures that the resource allocation does not fluctuate too much in the operation process and is allocated for a moment and recovered; and analyzing when the resource demand is reduced, and if the resources are often required to be improved in a short period after the resources are reduced, temporarily not recovering the resources, and keeping the system stable.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention will be described in detail with reference to the following figures and examples:
as shown in fig. 1, a method for predicting a load of an operation and maintenance system based on active detection includes: the system resource monitoring module, the resource prediction module, the resource comparison module, the resource allocation module, the allocation recording module, the allocation judgment module and the active detection module, the method comprises the following steps:
(1) And monitoring the resource use condition of the operation and maintenance system in real time through a system resource monitoring module, and sending the current resource use condition to a resource prediction module according to a certain time period (for example, every 10 minutes). It should be noted that the resources in the method include resources such as a hard disk and a CPU, and do not include memory resources, and when the resources include multiple types, the method analyzes each resource respectively, and provides support for allocation of CPU resources, storage resources, and the like.
(2) And predicting the resource demand of the next time period through the resource prediction module, wherein the resource prediction is developed in the present stage and is mature, and models such as hidden Markov are used for predicting the resource demand. And setting the current resource as Z0 and the predicted resource demand as Z1.
(3) Judging through a resource comparison module, judging whether Z1 is larger than Z0, if so, turning to the step (4), and otherwise, turning to the step (5);
(4) The resource allocation module allocates resources of the next stage according to the predicted resource demand Z1 (at this time, Z1 is larger than Z0), and modifies the allocation recording module, wherein the modification method comprises the following steps:
assume the original list of records is: d1 D2, d3, … … dm, the modified record list is: d1 D2, d3, … … dm,1; where dm represents the record of the mth resource allocation, and its value is only 0 or 1 (i.e. the determination flag a is 1, and the determination flag B is 0), 0 represents that the resource is decreased, and 1 represents that the resource is increased or unchanged.
(5) Calling an active detection module, calling an allocation judgment module by the active detection module, traversing a record list in the allocation record module by the allocation judgment module, if the number of 1's after the first 0's in the record list is larger than the number of 0's after the first 0's, not reallocating resources (keeping Z0, at this time, Z1 is smaller than Z0), otherwise, turning to the step (6);
such as: the recording list is: 0101011 followed by 4 1,2 0's for the first 0; the condition is satisfied and no resource reallocation is performed.
Such as: the recording list is: 1011000 followed by 3 0,2 1 for the first 0; if the condition is not met, the step (6) is carried out to require resource reallocation.
(6) The resource allocation module allocates the next-stage resource according to the predicted resource demand Z1 and modifies the allocation recording module, and the modification method comprises the following steps:
assume the original list of records is: d1 D2, d3, … … dm, the modified record list is: d1 D2, d3, … … dm,0; wherein dm represents the record of the m-th resource allocation, the value of which can only be 0 or 1,0 represents that the resource is reduced, and 1 represents that the resource is increased or unchanged.
It should be noted that: the recording list records a 0 or 1 in each resource allocation, i.e. the sequence is always followed by 0 and 1, so that each allocation knows whether the resource was added last time. Each resource allocation is the existing requirement, the increase is directly agreed (Z1 is larger than Z0), the 1 is added later, the decrease is needed, whether the condition is met or not is firstly seen, the condition is met, the reallocation-allocation as required is carried out, and otherwise, the reallocation (recovery) is not carried out even if the demand is less.
It should be understood that equivalent substitutions and changes to the technical solution and the inventive concept of the present invention should be made by those skilled in the art to the protection scope of the appended claims.

Claims (3)

1. An operation and maintenance system load prediction method based on active detection is characterized in that: the method comprises the following specific steps:
(1) Monitoring the resource use condition of the operation and maintenance system in real time through a system resource monitoring module, and sending the current resource use condition to a resource prediction module according to a certain time period;
(2) Predicting the resource demand of the next time period through a resource prediction module, and setting the current resource as Z0 and the predicted resource demand as Z1;
(3) Judging through a resource comparison module, judging whether Z1 is larger than Z0, if so, turning to the step (4), otherwise, turning to the step (5);
(4) The resource allocation module allocates resources of the next stage according to the predicted resource demand Z1, modifies the allocation recording module, and adds a judgment mark A in the original recording list, wherein the judgment mark A indicates that the resources are increased or unchanged;
assume the original list of records is: d1, d2, d3, … … dm, the modified record list is: d1, d2, d3, … … dm,1; wherein dm represents the record of the mth resource allocation, the value of dm can only be 0 or 1,0 represents that the resource is reduced, and 1 represents that the resource is increased or unchanged;
(5) Calling an active detection module, calling an allocation judgment module by the active detection module, traversing a record list in the allocation record module by the allocation judgment module, constructing a judgment condition according to a judgment mark A and a judgment mark B, if the judgment condition is met, not reallocating resources, and otherwise, turning to the step (6);
the judgment conditions are as follows: if the number of 1 after the first 0 in the recording list is larger than the number of 0 after the first 0, the resource is not reallocated;
(6) And the resource allocation module allocates the next-stage resource according to the predicted resource demand Z1, modifies the allocation recording module, and adds a judgment mark B in the original recording list, wherein the judgment mark B indicates that the resource is reduced.
2. The active probing based operation and maintenance system load prediction method of claim 1, wherein: the resources comprise hard disks or CPUs, and when the resources comprise multiple types, each resource is analyzed and predicted respectively.
3. The active probing based operation and maintenance system load prediction method of claim 2, wherein: in the step (6), the method for modifying the distribution record module comprises the following steps:
assume the original list of records is: d1, d2, d3, … … dm, the modified record list is: d1, d2, d3, … … dm,0; wherein dm represents the record of the m-th resource allocation, the value of which can only be 0 or 1,0 represents that the resource is reduced, and 1 represents that the resource is increased or unchanged.
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CN109165093A (en) * 2018-07-31 2019-01-08 宁波积幂信息科技有限公司 A kind of calculate node cluster elasticity distribution system and method

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Publication number Priority date Publication date Assignee Title
CN102664814A (en) * 2012-05-17 2012-09-12 西安交通大学 Grey-prediction-based adaptive dynamic resource allocation method for virtual network
CN104391737A (en) * 2014-12-15 2015-03-04 成都英力拓信息技术有限公司 Method for optimizing load balance in cloud platform
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