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Cloud Task Scheduling Method Based on Phagocytosis Particle Swarm Genetic Hybrid Algorithm

A technology of task scheduling and mixed algorithms, applied in genetic rules, neural learning methods, calculations, etc., can solve the problems that cannot be solved better, there is no free lunch algorithm, etc., to ensure diversity, improve overall completion time, The effect of reducing the possibility of getting stuck in a local optimum

Active Publication Date: 2022-02-11
INNER MONGOLIA AGRICULTURAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] According to the "no free lunch" principle, it shows that no algorithm can fully satisfy all application scenarios, only "a certain algorithm has the best effect under certain conditions"
Therefore, when solving task scheduling problems, only using a single algorithm often cannot solve the problem well, so it is necessary to try to integrate different algorithms

Method used

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  • Cloud Task Scheduling Method Based on Phagocytosis Particle Swarm Genetic Hybrid Algorithm

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Effect test

Embodiment 1

[0091] In the case of different task numbers, the cloud task scheduling method based on the particle swarm genetic hybrid algorithm (PSO_PGA) of phagocytosis in the present invention is combined with particle swarm algorithm (PSO), FIFO scheduling strategy, particle swarm algorithm improved algorithm (PSO_CM), enhanced Comparing the genetic particle swarm hybrid algorithm (GA_EPSO) and the improved genetic algorithm (IGA) in terms of task completion time in cloud task scheduling;

[0092] Use 10 virtual machines and ensure that the number of virtual machines remains unchanged, the number of tasks is from 60-600, ensure that the number of iterations of each algorithm is the same and remain unchanged, repeat the calculation and take the average value, and verify the performance of the PSO_PGA algorithm in terms of task completion time The advantages and disadvantages of the algorithm; the specific algorithm parameter settings are shown in Table 1, and the results are attached f...

Embodiment 2

[0097] In the case of ensuring the same batch of tasks and the same number of tasks and different iterations, the cloud task scheduling method based on the particle swarm genetic hybrid algorithm (PSO_PGA) of the present invention and the improved particle swarm algorithm (PSO_CM) algorithm (PSO_CM), enhanced genetic The particle swarm hybrid algorithm (GA_EPSO) and the improved genetic algorithm (IGA) compare and verify the convergence accuracy of the cloud task scheduling method in the present invention;

[0098] Using 10 virtual machines, the number of cloud tasks is 300 and the same batch of cloud tasks. Ensure that the number of virtual machines and cloud tasks remains unchanged, change the number of iterations of the algorithm, repeat multiple times to take the average value, and verify the performance of the PSO_PGA algorithm in terms of convergence accuracy. Concrete algorithm parameter is identical with embodiment one, and result is attached image 3 shown.

Embodiment 3

[0100] Guarantee that the number of cloud tasks is constant but not the same batch of tasks, ensure that the number of tasks is the same but the task batches are different, and in the case of different iterations, the particle swarm genetic hybrid algorithm (PSO_PGA) based on phagocytosis in the present invention Cloud task scheduling method compares and verifies cloud task scheduling in the present invention with particle swarm algorithm (PSO), improved particle swarm algorithm (PSO_CM), enhanced genetic particle swarm hybrid algorithm (GA_EPSO) and improved genetic algorithm (IGA) The convergence accuracy of the method;

[0101] Using 10 virtual machines, the number of cloud tasks is 300. When the number of cloud tasks is kept constant but the task batches are different, the number of iterations of the algorithm is changed, and the average value is repeated several times to verify the performance of the PSO_PGA algorithm in terms of convergence accuracy. Pros and cons. Conc...

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Abstract

The invention discloses a cloud task scheduling method based on a phagocytosis particle swarm genetic hybrid algorithm. Aiming at the task scheduling problem in the cloud environment, the updating method of position and speed in the standard particle swarm algorithm is changed, and the fitness function and the load balancing standard deviation are used to pair Each generation of particle swarms is divided into primary and secondary divisions, and the final divided particle sub-populations are subjected to phagocytosis mutation and cross-mutation operations respectively, and the scheduling scheme of cloud tasks is obtained. Through simulation experiments, the cloud task scheduling method proposed in the present invention is compared with several other existing cloud task scheduling methods. The results show that the method in the present invention significantly improves the overall completion time of cloud tasks and has higher convergence. The accuracy proves the effectiveness of the cloud task scheduling method based on the phagocytosis particle swarm genetic hybrid algorithm in the present invention.

Description

technical field [0001] The invention relates to the field of cloud technology, in particular to a cloud task scheduling method based on a phagocytosis-based particle swarm genetic hybrid algorithm. Background technique [0002] Cloud computing is defined as an information technology source and delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) Management work or service provider interactions are quickly provided and released. The delivery models of cloud computing are divided into three categories, namely software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS). [0003] As an emerging technology, cloud computing is widely used by companies and enterprises because of its great commercial value. It rationally utilizes and allocates various resources provided by the cloud environment, effectively schedules m...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50G06N3/12
CPCG06F9/5088G06N3/126G06F9/4881G06F9/5066G06N3/006G06N3/088G06N20/00G06F9/5072G06F9/5083G06F9/48
Inventor 付学良孙扬李宏慧
Owner INNER MONGOLIA AGRICULTURAL UNIVERSITY
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