TY - JOUR
T1 - Energy Consumption and Performance Optimized Task Scheduling in Distributed Data Centers
AU - Yuan, Haitao
AU - Bi, Jing
AU - Zhang, Jia
AU - Zhou, Meng Chu
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62173013, Grant 62073005, and Grant 61802015; and in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005.
Publisher Copyright:
© 2013 IEEE.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
KW - Biobjective optimization
KW - cloud data centers (DCs)
KW - differential evolution (DE)
KW - energy optimization
KW - resource allocation
KW - simulated annealing (SA)
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U2 - 10.1109/TSMC.2021.3128430
DO - 10.1109/TSMC.2021.3128430
M3 - Article
AN - SCOPUS:85120045110
SN - 2168-2216
VL - 52
SP - 5506
EP - 5517
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 9
ER -