"Energy Consumption and Performance Optimized Task Scheduling in Distri" by Haitao Yuan, Jing Bi et al.
 

Energy Consumption and Performance Optimized Task Scheduling in Distributed Data Centers

Document Type

Article

Publication Date

9-1-2022

Abstract

A growing number of organizations are hosting their software applications in distributed data centers (DCs) in the cloud, for faster response time and higher energy efficiency. The dramatic increase of user tasks, however, poses a significant challenge on DC providers to retain users' expectations on both aspects. To tackle this challenge, this work first formulates the problem into a constrained biobjective optimization problem. A biobjective algorithm, named simulated-annealing-based adaptive differential evolution (SADE), is presented to simultaneously reduce both the response time of tasks and energy cost. Meanwhile, a method of minimal Manhattan distance is adopted to search for a final knee, for achieving a good balance between response time minimization and energy cost reduction. Experimental results on real-life datasets, i.e., the electricity prices and tasks collected from a Google cluster trace, have proved that SADE yields less task response time and lower energy cost compared with state-of-the-art algorithms.

Identifier

85120045110 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Systems

External Full Text Location

https://doi.org/10.1109/TSMC.2021.3128430

e-ISSN

21682232

ISSN

21682216

First Page

5506

Last Page

5517

Issue

9

Volume

52

Grant

61802015

Fund Ref

National Natural Science Foundation of China

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