An Energy-Efficient Task Scheduling Heuristic Algorithm Without Virtual Machine Migration in Real-Time Cloud Environments

  • Yi ZhangEmail author
  • Liuhua Chen
  • Haiying Shen
  • Xiaohui Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9955)


Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical machines (PMs) and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new PMs. In this paper, we propose a heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm. EDA-NMS exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.


Virtualized cloud Real-time task Scheduling Criticality Energy-aware 



The work on this paper has been supported by Scientific and Technological Research Program for Guangxi Educational Commission grants \(\sharp \)2013YB113, Guangxi Universities key Laboratory Fund of Embedded Technology and Intelligent Information Processing.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yi Zhang
    • 1
    Email author
  • Liuhua Chen
    • 2
  • Haiying Shen
    • 2
  • Xiaohui Cheng
    • 1
  1. 1.School of Information and EngineeringGuilin University of TechnologyGuangxiChina
  2. 2.School of Electrical and Computer EngineeringClemson UniversityClemsonUSA

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