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

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 mor...

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Published in:	Journal of grid computing Vol. 14; no. 1; pp. 55 - 74
Main Authors:	Tang, Zhuo, Qi, Ling, Cheng, Zhenzhen, Li, Kenli, Khan, Samee U., Li, Keqin
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.03.2016
Subjects:	Algorithms Cloud computing Computer Science Data centers Energy management Management of Computing and Information Systems Processor Architectures Processors Task scheduling Tasks User Interfaces and Human Computer Interaction Workflow Cloud computing Heterogeneous Heuristic algorithm Energy saving scheduling DVFS
ISSN:	1570-7873, 1572-9184
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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.
AbstractList	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.
Author	Li, Keqin Cheng, Zhenzhen Li, Kenli Khan, Samee U. Qi, Ling Tang, Zhuo
Author_xml	– sequence: 1 givenname: Zhuo surname: Tang fullname: Tang, Zhuo email: ztang@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University – sequence: 2 givenname: Ling surname: Qi fullname: Qi, Ling organization: College of Information Science and Engineering, Hunan University – sequence: 3 givenname: Zhenzhen surname: Cheng fullname: Cheng, Zhenzhen organization: College of Information Science and Engineering, Hunan University – sequence: 4 givenname: Kenli surname: Li fullname: Li, Kenli organization: College of Information Science and Engineering, Hunan University – sequence: 5 givenname: Samee U. surname: Khan fullname: Khan, Samee U. organization: Department of Electrical and Computer Engineering, North Dakota State University – sequence: 6 givenname: Keqin surname: Li fullname: Li, Keqin organization: College of Information Science and Engineering, Hunan University, Department of Computer Science, State University of New York
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Snippet	The growth of energy consumption has been explosive in current data centers, super computers, and public cloud systems. This explosion has led to greater...
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SubjectTerms	Algorithms Cloud computing Computer Science Data centers Energy management Management of Computing and Information Systems Processor Architectures Processors Task scheduling Tasks User Interfaces and Human Computer Interaction Workflow
Title	An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment
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