Scheduling Based on Improved Genetic Algorithm and Difference Algorithm in Cloud Computing

GE Jun-wei; SUN Fang-fang; FANG Yi-qiu

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Scheduling Based on Improved Genetic Algorithm and Difference Algorithm in Cloud Computing

更新时间：2026-09-04

- Scheduling Based on Improved Genetic Algorithm and Difference Algorithm in Cloud Computing
- Vol. 33, Issue 11, Pages: 5-9(2016)
- 作者机构：
  
  1. 重庆邮电大学机构软件工程学院,重庆,400065
  2. 重庆邮电大学图书馆,重庆,400065
  3. 重庆邮电大学计算机学院,重庆,400065
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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葛君伟, 孙方方, 方义秋. 基于遗传加差分算法的云计算任务调度[J]. 微电子学与计算机, 2016, 33(11): 5-9.

GE Jun-wei, SUN Fang-fang, FANG Yi-qiu. Scheduling Based on Improved Genetic Algorithm and Difference Algorithm in Cloud Computing[J]. 2016, 33(11): 5-9.
葛君伟, 孙方方, 方义秋. 基于遗传加差分算法的云计算任务调度[J]. 微电子学与计算机, 2016, 33(11): 5-9. DOI：

GE Jun-wei, SUN Fang-fang, FANG Yi-qiu. Scheduling Based on Improved Genetic Algorithm and Difference Algorithm in Cloud Computing[J]. 2016, 33(11): 5-9. DOI：

摘要

随着云计算的不断发展

任务调度问题成了研究的难点.如何快速处理用户的任务请求

使得云计算中资源达到负载均衡

使任务的完成时间及成本达到相对最优

通过对比分析了已有任务调度算法存在的问题提出遗传加差分算法的云计算任务调度策略

采用种群更新的方式提高算法的鲁棒性、二级变异策略提高种群多样性

加快算法的收敛

加入差分算子提高算法的局部寻优能力.通过cloudsim仿真实验并对比遗传算法、差分算法、min-min算法证明了本算法的在负载均衡度

时间和成本上有效性.

Abstract

With the continuous development of cloud computing

task scheduling problem become a crucial aspect. How to deal with tasks quickly

not only meet the needs of users

but also to achieve load balancing and make the completion time

cost to achieve relatively optimal.By comparing and analyzing the existing task scheduling algorithms.we proposed the genetic and difference algorithm policy to solve the problem. we employs population updating scheme to improve the robustness of the algorithm.Using secondary mutation strategy to improve population diversity

accelerate the convergence speed. Adding difference operator to improve the algorithms local search ability.The performance is analyzed using Cloudsim simulator and compared with existing GA

Min-min algorithm. Simulation results demonstrate that the proposed algorithm has better performance in load balancing

finish time and costs.

关键词

Keywords

references

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Calheiros R N, Ranjan R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software-Practice and Experience, 2011, 41(1): 23-50. DOI: 23

Epitropakis M G, Tasoulis D K, Pavlidis N G, et al. Enhancing differential evolution utilizing proximity-based mutation operators[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 67-98. DOI: 67

李建锋, 彭舰. 云计算环境下基于改进遗传算法的任务调度算法[J]. 计算机应用, 2011, 31(1): 184-186. DOI: 184

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Mezmaz M, Melab N, Kessaci Y, et al. A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems[J]. Journal of Parallel % Distributed Computing, 2011, 71(11): 1497-1508. DOI: 1497

Chen S, Wu J, Lu Z. A cloud computing resource scheduling policy based on genetic algorithm with multiple fitness[C]// Computer and Information Technology, International Conference on. Chittagong, Bangladesh, IEEE, 2012: 177-184.

熊聪聪, 冯龙, 陈丽仙, 等. 云计算中基于遗传算法的任务调度算法研究[J]. 华中科技大学学报: 自然科学版, 2012(Sup1): 1-4. DOI: 1

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