Cloud Resource Schedling Strategy Based on Improved Particle Swarm Optimization

CAI Xiao-li; QIAN Cheng

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Cloud Resource Schedling Strategy Based on Improved Particle Swarm Optimization

更新时间：2026-09-04

- Cloud Resource Schedling Strategy Based on Improved Particle Swarm Optimization
- Vol. 35, Issue 6, Pages: 28-30(2018)
- 作者机构：
  
  常州工学院机构计算机信息工程学院,江苏,常州,213002
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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蔡晓丽, 钱诚. 基于改进的粒子群算法的云资源调度策略[J]. 微电子学与计算机, 2018, 35(6): 28-30, 35.

CAI Xiao-li, QIAN Cheng. Cloud Resource Schedling Strategy Based on Improved Particle Swarm Optimization[J]. 2018, 35(6): 28-30.
蔡晓丽, 钱诚. 基于改进的粒子群算法的云资源调度策略[J]. 微电子学与计算机, 2018, 35(6): 28-30, 35. DOI：

CAI Xiao-li, QIAN Cheng. Cloud Resource Schedling Strategy Based on Improved Particle Swarm Optimization[J]. 2018, 35(6): 28-30. DOI：

摘要

云计算资源的调度是云计算中的一项关键技术.针对粒子群算法存在易陷入局部最优解和"早熟"的缺陷

提出一种改进的粒子群算法.通过改进粒子迭代过程中社会项系数和认知项系数的权重变化

使算法更符合最优解的求解规律

避免陷入局部最优解.仿真实验表明

改进后的粒子群算法适应度更强、收敛速度更快

具有更强的全局搜索能力.该算法可以有效提高云计算资源的利用率

具有良好的应用价值.

Abstract

Cloud resource scheduling strategy is the most important technology in cloud computing. In order to solve the problem of being easy to to fall into locally optimal solution

we proposed an improved particle swarm optimization algorithm (IPSO).Coefficient about social and personal cognition are changed according to the times of iteration

which can comply with objective rules much better and avoid falling to locally optimal solution. Simulation results show that IPSO can improve the availability of cloud resources.It has higher global search ability

better fitness

and good application value.

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references

Iosup A, Ostermann S, Yigitbasi M N, et al. Performance analysis of cloud computing services for many-tasks scientific computing[J]. IEEE Transactions on Parallel % Distributed Systems, 2011, 22(6):931-945. DOI: 931

孟令玺, 李洪亮.基于CA-PSO算法的云计算资源调度策略[J].计算机仿真, 2013, 30(10):406-410. DOI: 406

孙大为, 常桂然, 李凤云, 等.一种基于免疫克隆的偏好多维QoS云资源调度优化算法[J].电子学报, 2011, 39(8):1824-1831. DOI: 1824

刘卫宁, 靳洪兵, 刘波.基于改进量子遗传算法的云计算资源调度[J].计算机应用, 2013, 33(8):2151-2153. DOI: 2151

袁正午, 李君琪.基于改进粒子群算法的云资源调度[J].计算机工程与设计, 2016, 37(2):401-404. DOI: 401

Lee Y C, Zomaya A Y. Energy efficient utilization of resources in cloud computing systems[J]. Journal of Supercomputing, 2012, 60(2):268-280. DOI: 268

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