Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers

Ziqian Dong, Ning Liu, Roberto Rojas-Cessa

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

In this paper, we introduce a model of task scheduling for a cloud-computing data center to analyze energy-efficient task scheduling. We formulate the assignments of tasks to servers as an integer-programming problem with the objective of minimizing the energy consumed by the servers of the data center. We prove that the use of a greedy task scheduler bounds the constraint service time whilst minimizing the number of active servers. As a practical approach, we propose the most-efficient-server-first task-scheduling scheme to minimize energy consumption of servers in a data center. Most-efficient-server-first schedules tasks to a minimum number of servers while keeping the data-center response time within a maximum constraint. We also prove the stability of most-efficient-server-first scheme for tasks with exponentially distributed, independent, and identically distributed arrivals. Simulation results show that the server energy consumption of the proposed most-efficient-server-first scheduling scheme is 70 times lower than that of a random-based task-scheduling scheme.

Original languageEnglish (US)
JournalJournal of Cloud Computing
Volume4
Issue number1
DOIs
StatePublished - Dec 1 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Keywords

  • Cloud computing
  • Data center
  • Energy efficiency
  • Greedy algorithm
  • Integer programming

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