Task scheduling and server provisioning for energy-efficient cloud-computing data centers

Ning Liu, Ziqian Dong, Roberto Rojas-Cessa

Research output: Contribution to conferencePaperpeer-review

35 Scopus citations

Abstract

In this paper, we present an optimization model for task scheduling for minimizing energy consumption in cloud-computing data centers. The proposed approach was formulated as an integer programming problem to minimize the cloud-computing data center energy consumption by scheduling tasks to a minimum numbers of servers while keeping the task response time constraints. We prove that the average task response time and the number of active servers needed to meet such time constraints are bounded through the use of a greedy task-scheduling scheme. In addition, we propose the most-efficient server- first task-scheduling scheme to minimize energy expenditure as a practical scheduling scheme. We model and simulate the proposed scheduling scheme for a data center with heterogeneous tasks. The simulation results show that the proposed taskscheduling scheme reduces server energy consumption on average over 70 times when compared to the energy consumed under a (not-optimized) random-based task-scheduling scheme. We show that energy savings are achieved by minimizing the allocated number of servers.

Original languageEnglish (US)
Pages226-231
Number of pages6
DOIs
StatePublished - 2013
Event33rd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2013 - Philadelphia, PA, United States
Duration: Jul 8 2013Jul 11 2013

Other

Other33rd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2013
CountryUnited States
CityPhiladelphia, PA
Period7/8/137/11/13

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Keywords

  • Cloud computing
  • Energy
  • Greedy Algorithm
  • Green data centers
  • Integer Programming
  • Task Scheduling

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