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A cloud service resource collaborative optimization scheduling method, system, medium and equipment

A collaborative optimization and scheduling method technology, applied in the field of cloud computing, can solve problems such as poor load balancing, slow convergence speed, weak optimization ability, etc., to improve rationality and effectiveness, improve service quality, and optimize resource utilization Effect

Active Publication Date: 2022-05-31
山东管理学院
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  • Claims
  • Application Information

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Problems solved by technology

The disadvantage is that it fails to consider the number of tasks and the large number of computing resources in the cloud computing environment, and the PSO algorithm has defects in the application of discretization problems; some researchers have also proposed the cross mutation operator in the weighted adaptive genetic algorithm based on the number of iterations, And load balance is studied as an important factor in judging the scheduling strategy, and an improved resource scheduling algorithm is proposed. The problem of poor load balancing caused by using task execution time as the fitness standard is that the method of weighting the number of iterations still has the problem of slow evolution rate in the later stage of evolution, slow convergence speed, and insufficient consideration of influencing factors

Method used

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  • A cloud service resource collaborative optimization scheduling method, system, medium and equipment
  • A cloud service resource collaborative optimization scheduling method, system, medium and equipment
  • A cloud service resource collaborative optimization scheduling method, system, medium and equipment

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Embodiment 1

[0032] As described in the background art, as more and more digital resources are deployed in the cloud, a reasonable task scheduling algorithm has become an important part of the cloud platform, and rational allocation of computing resources has become the key to solving the problem of resource scheduling.

[0033] In the optimization scheduling problem of cloud computing resources, the completion time of the task largely determines the quality of service, but it is one-sided to judge the quality of the scheduling strategy simply by the completion time of the task, and the load of computing resources and bandwidth should be considered accordingly. utilization.

[0034] Aiming at the problems existing in the adaptive genetic algorithm, this embodiment improves the adaptive crossover and mutation operators, and proposes a collaborative optimization scheduling algorithm of cloud service resources based on improved genetic algorithm. genetic algorithm, OSIG), such as figure 1 As...

Embodiment 2

[0089] Embodiment 2 of the present disclosure provides a cloud service resource collaborative optimization scheduling system, including:

[0090] The data preprocessing module is configured to: obtain computing tasks and computing node resources, and decompose the obtained computing tasks to obtain multiple subtasks of the same size;

[0091] The scheduling strategy acquisition module is configured to: input the obtained subtasks and computing node resources into the preset genetic algorithm model, and use the fitness function constructed according to the task execution time, the calculation load balance and the task transmission time to evaluate the scheduling strategy , to obtain the optimal task scheduling strategy;

[0092] The task assignment module is configured to: according to the obtained optimal scheduling policy, assign the subtasks to the corresponding computing nodes.

[0093] The working method of the system is the same as the optimal scheduling method described...

Embodiment 3

[0095] A third aspect of the present disclosure provides a medium on which a program is stored, and when the program is executed by a processor, implements the steps in the cloud service resource collaborative optimization scheduling method described in Embodiment 1 of the present disclosure.

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Abstract

The present disclosure provides a method, system, medium, and equipment for collaborative optimization scheduling of cloud service resources, which belong to the technical field of cloud computing. Computing tasks and computing node resources are obtained, and the obtained computing tasks are decomposed to obtain multiple subtasks of the same size. ; Input the obtained subtasks and computing node resources into the preset genetic algorithm model, use the fitness function constructed according to the task execution time, computing load balance and task transmission time to evaluate the scheduling strategy, and obtain the optimal task scheduling strategy ; According to the obtained optimal scheduling strategy, the subtasks are allocated to the corresponding computing nodes; while shortening the task completion time, the present disclosure makes the utilization rate of cloud service resources optimal, and improves the service quality of the cloud service system.

Description

technical field [0001] The present disclosure relates to the technical field of cloud computing, and in particular, to a method, system, medium and device for collaborative optimization and scheduling of cloud service resources. Background technique [0002] The statements in this section merely provide background related to the present disclosure and do not necessarily constitute prior art. [0003] With the widespread application of cloud computing technology, more and more digital resources are deployed in the cloud, and the corresponding resource optimization scheduling mechanism has become a key technology restricting its further promotion. Aiming at the problem that cloud computing systems need to take into account a wider range of cloud service resource types and their coordinated optimal scheduling, the present invention studies a cloud computing architecture and management mechanism that takes into account security, reliability and scalability, and can carry more re...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L67/1001H04L67/63G06N3/12
CPCG06N3/126H04L67/1001H04L67/63Y02D10/00
Inventor 王宁刘少杰
Owner 山东管理学院
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