Task scheduling based on virtual machine matching in clouds

Peiyun Zhang, Mengchu Zhou

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

1 Scopus citations

Abstract

This work proposes a task scheduling method based on virtual machine (VM) matching in clouds. Its objectives are 1) to maximize task scheduling performance and 2) to minimize non-reasonable task allocation, e.g., a simple task to a high-performance VM and thus causing resource waste. A job classifier is utilized to classify tasks and match to a most suitable VM. This work uses the historical data to pre-create VMs of different types. This can save time of creating VMs during task scheduling. Tasks are efficiently matched with concrete VMs dynamically. Task scheduling is accordingly conducted. Experimental results with the Google Cluster Trace dataset show that the proposed method can effectively improve the cloud's task scheduling performance and achieve desired load balancing among various virtual machines in comparison with some existing methods.

Original languageEnglish (US)
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages618-623
Number of pages6
ISBN (Electronic)9781509067800
DOIs
StatePublished - Jul 1 2017
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: Aug 20 2017Aug 23 2017

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2017-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country/TerritoryChina
CityXi'an
Period8/20/178/23/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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