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Journal of Grid Computing

, Volume 14, Issue 1, pp 55–74 | Cite as

An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment

  • Zhuo TangEmail author
  • Ling Qi
  • Zhenzhen Cheng
  • Kenli Li
  • Samee U. Khan
  • Keqin Li
Article

Abstract

The growth of energy consumption has been explosive in current data centers, super computers, and public cloud systems. This explosion has led to greater advocacy of green computing, and many efforts and works focus on the task scheduling in order to reduce energy dissipation. In order to obtain more energy reduction as well as maintain the quality of service by meeting the deadlines, this paper proposes a DVFS-enabled Energy-efficient Workflow Task Scheduling algorithm: DEWTS. Through merging the relatively inefficient processors by reclaiming the slack time, DEWTS can leverage the useful slack time recurrently after severs are merged. DEWTS firstly calculates the initial scheduling order of all tasks, and obtains the whole makespan and deadline based on Heterogeneous-Earliest-Finish-Time (HEFT) algorithm. Through resorting the processors with their running task number and energy utilization, the underutilized processors can be merged by closing the last node and redistributing the assigned tasks on it. Finally, in the task slacking phase, the tasks can be distributed in the idle slots under a lower voltage and frequency using DVFS technique, without violating the dependency constraints and increasing the slacked makespan. Based on the amount of randomly generated DAGs workflows, the experimental results show that DEWTS can reduce the total power consumption by up to 46.5 % for various parallel applications as well as balance the scheduling performance.

Keywords

Cloud computing DVFS Energy saving scheduling Heterogeneous Heuristic algorithm 

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Zhuo Tang
    • 1
    Email author
  • Ling Qi
    • 1
  • Zhenzhen Cheng
    • 1
  • Kenli Li
    • 1
  • Samee U. Khan
    • 2
  • Keqin Li
    • 1
    • 3
  1. 1.College of Information Science and EngineeringHunan UniversityChangshaChina
  2. 2.Department of Electrical and Computer EngineeringNorth Dakota State UniversityFargoUSA
  3. 3.Department of Computer ScienceState University of New YorkNew PaltzUSA

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