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

Haitao Yuan, Jing Bi, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728168531
DOIs
StatePublished - Oct 30 2020
Event2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 - Nanjing, China
Duration: Oct 30 2020Nov 2 2020

Publication series

Name2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

Conference

Conference2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
Country/TerritoryChina
CityNanjing
Period10/30/2011/2/20

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

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