Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds

Haitao Yuan; Jing Bi; Meng Chu Zhou

doi:10.1109/ICNSC48988.2020.9238080

Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds

Haitao Yuan, Jing Bi, Meng Chu Zhou

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728168531
DOIs	https://doi.org/10.1109/ICNSC48988.2020.9238080
State	Published - Oct 30 2020
Event	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 - Nanjing, China Duration: Oct 30 2020 → Nov 2 2020

Publication series

Name	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

Conference

Conference	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
Country/Territory	China
City	Nanjing
Period	10/30/20 → 11/2/20

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Statistics, Probability and Uncertainty
Control and Optimization
Sensory Systems

Keywords

Data centers
cloud computing
multi-objective optimization
performance modeling
task scheduling

Access to Document

10.1109/ICNSC48988.2020.9238080

Cite this

Yuan, H., Bi, J., & Zhou, M. C. (2020). Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds. In 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 [9238080] (2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNSC48988.2020.9238080

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abstract = "Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.",

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note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61802015 and 61703011, in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005, and in part by the National Defense Pre-Research Foundation of China under Grants 41401020401 and 41401050102. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 ; Conference date: 30-10-2020 Through 02-11-2020",

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Yuan, H, Bi, J & Zhou, MC 2020, Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds. in 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020., 9238080, 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020, Nanjing, China, 10/30/20. https://doi.org/10.1109/ICNSC48988.2020.9238080

Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds. / Yuan, Haitao; Bi, Jing; Zhou, Meng Chu.

2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9238080 (2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Bi, Jing

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N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61802015 and 61703011, in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005, and in part by the National Defense Pre-Research Foundation of China under Grants 41401020401 and 41401050102. Publisher Copyright: © 2020 IEEE.

PY - 2020/10/30

Y1 - 2020/10/30

N2 - Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.

AB - Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.

KW - Data centers

KW - cloud computing

KW - multi-objective optimization

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KW - task scheduling

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 30 October 2020 through 2 November 2020

ER -

Yuan H, Bi J, Zhou MC. Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds. In 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9238080. (2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020). doi: 10.1109/ICNSC48988.2020.9238080

Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds

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All Science Journal Classification (ASJC) codes

Keywords

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