计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于离散粒子群优化的云计算QoS调度算法

王月1,2,刘亚秋1,2,郭继峰1,2,景维鹏1,2   

  1. (1.东北林业大学 信息与计算机工程学院,哈尔滨 150040;2.黑龙江省林业生态大数据存储与高性能(云)计算工程技术研究中心,哈尔滨 150040)
  • 收稿日期:2016-09-02 出版日期:2017-06-15 发布日期:2017-06-15
  • 作者简介:王月(1991—),女,硕士研究生,主研方向为云计算、智能优化算法;刘亚秋(通信作者),教授、博士生导师;郭继峰、景维鹏,副教授、博士。
  • 基金项目:

    国家自然科学基金(31370565);哈尔滨市科技创新人才研究专项(2015RAYXJ005)。

QoS Scheduling Algorithm in Cloud Computing Based on Discrete Particle Swarm Optimization

WANG Yue 1,2,LIU Yaqiu 1,2,GUO Jifeng 1,2,JING Weipeng 1,2   

  1. (1.College of Information and Computer Engineering,Northeast Forestry University,Harbin 150040,China; 2.Heilongjiang Province Engineering Technology Research Center for Forestry Ecological Big Data Storage and High Performance Computing(Cloud Computing),Harbin 150040,China)
  • Received:2016-09-02 Online:2017-06-15 Published:2017-06-15

摘要:

针对云计算环境下用户任务的多种服务质量(QoS)需求,综合考虑任务截止时间、调度预算和可靠性,提出一种多QoS约束离散粒子群优化(QoS-DPSO)的任务调度算法。对任务的QoS进行定义和数学建模,通过截止时间和调度预算约束DPSO的搜索空间,根据可靠性重新定义DPSO的适应度函数,由适应度值搜索最优的任务调度方案。实验结果表明,与PSO,DPSO,DBC和EDF算法相比,QoS-DPSO在满足调度截止期的情况下具有较高的可靠性,并且对Makespan性能的影响较小。

关键词: 云计算, 服务质量, 离散粒子群优化, 截止时间, 调度预算, 可靠性

Abstract:

In order to meet the varied Quality of Service(QoS) requirements of user tasks under cloud computing environment,taking into account of task deadline,scheduling budget and reliability,this paper proposes a task scheduling algorithm with multi-QoS constraints based on Discrete Particle Swarm Optimization(QoS-DPSO).Firstly,the QoS of the task is defined and its mathematical model is established.Then,the search space of DPSO is constrained according to the deadline and scheduling budget,and the fitness function of DPSO is redefined with reliability.Finally,according to the fitness value,the optimal task scheduling scheme is searched out.Experimental result shows that,compared with PSO,DPSO,DBC and EDF algorithm,QoS-DPSO has higher reliability in the case of meeting the deadline.Meanwhile,it has less influence on the performance of Makespan.

Key words: cloud computing, Quality of Service(QoS), Discrete Particle Swarm Optimization(DPSO), deadline, scheduling budget, reliability

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