Cloud scheduling with setup cost

Yossi Azar, Nikhil R. Devanur, Navendu Jain

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we investigate the problem of online task scheduling of jobs such as MapReduce jobs, Monte Carlo simulations and generating search index from web documents, on cloud computing infrastructures. We consider the virtualized cloud computing setup comprising machines that host multiple identical virtual machines (VMs) under payas-you-go charging, and that booting a VM requires a constant setup time. The cost of job computation depends on the number of VMs activated, and the VMs can be activated and shutdown on demand. We propose a new bi-objective algorithm to minimize the maximum task delay, and the total cost of the computation. We study both the clairvoyant case, where the duration of each task is known upon its arrival, and the more realistic non-clairvoyant case.

Original languageEnglish
Title of host publicationSPAA 2013 - Proceedings of the 25th ACM Symposium on Parallelism in Algorithms and Architectures
Pages298-304
Number of pages7
StatePublished - 2013
Event25th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2013 - Montreal, QC, Canada
Duration: 23 Jul 201325 Jul 2013

Publication series

NameAnnual ACM Symposium on Parallelism in Algorithms and Architectures

Conference

Conference25th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2013
Country/TerritoryCanada
CityMontreal, QC
Period23/07/1325/07/13

Keywords

  • Clairvoyant
  • Cloud computing
  • Competitive ratio
  • Non-clairvoyant
  • Online algorithms
  • Scheduling

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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