• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

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一种基于繁忙时间的并行调度能耗优化算法

蔡立军1,潘江波1,陈磊2,何庭钦1   

  1. (1.湖南大学信息科学与工程学院,湖南 长沙 410082;2.湖南大学电气与信息工程学院,湖南 长沙 410082)
  • 收稿日期:2015-10-10 修回日期:2015-12-30 出版日期:2017-01-25 发布日期:2017-01-25
  • 基金资助:

    基金项目:国家自然科学基金(61174140,61472127,61272395);湖南省自然科学基金(14JJ3107);湖南省教育厅科研优秀青年项目(15B087)

An energy-efficient algorithm based on
busy time in parallel scheduling

CAI Lijun1,PAN Jiangbo1,CHEN Lei2,HE Tingqin1   

  1. (1.College of Computer Science and Engineering,Hunan University,Changsha 410082;
    2.College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)
  • Received:2015-10-10 Revised:2015-12-30 Online:2017-01-25 Published:2017-01-25

摘要:

减少服务器繁忙时间是云计算并行调度中节约能耗的一种有效途径,而现有基于繁忙时间的能耗节约策略大多以牺牲作业调度性能为代价,无法与其他有调度性能优势的作业调度算法结合使用。提出一种有效的基于繁忙时间的并行调度能耗优化算法——BTEOA。首先,将作业请求队列根据当前服务器可用资源划分为作业窗口和非作业窗口。其次,按照作业窗口中作业请求能使所有服务器总繁忙时间局部最优的原则匹配服务器进行调度。最后,作业窗口中所有作业请求执行完成后,继续将非作业窗口进行作业窗口与非作业窗口划分,直到所有作业请求执行完毕。作业调度过程中,始终保持作业排队模型不变,保证了作业调度性能不受影响。实例分析与实验结果表明,BTEOA算法能够在不影响作业调度性能的前提下,节约能耗,同时支持与其他作业调度算法结合使用。

关键词: 并行调度, 能耗优化, 繁忙时间, 调度性能, 局部最优

Abstract:

Minimizing the busy time of servers is an effective way to save energy in parallel scheduling applications in cloud computing, however, most of the existing busy time energy saving strategies are at the expense of job scheduling performance, and they are unable to cooperate with other job scheduling algorithms. We propose an effective energyefficient optimization algorithm based on busy time in parallel scheduling, called BTEOA. Firstly, based on the available resources of current servers, we divide the job request queue into a special job window and a general job window. Secondly, according to the completion time of job requests in the special job window, we can execute these job requests in the servers so as to ensure that the total busy time of all servers is locally optimal. Finally, when the execution of all job requests in the special job window finishes completely, the BTEOA continues to divide the general job window into a new special job window and a new general job window until the remaining job requests are all executed completely. During the job scheduling, the BTEOA can ensure the job scheduling performance unaffected by keeping the original job queuing model static. Instances and experimental results demonstrate the performance of the BTEOA, which can save energy consumption while does not affect the performance of job scheduling. Besides, it can also be used jointly with other job scheduling algorithms.

 

Key words: