A novel VM workload prediction using grey forecasting model in cloud data center

Jhu Jyun Jheng, Fan Hsun Tseng, Han Chieh Chao, Li Der Chou

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

47 Scopus citations

Abstract

In recent years, the resource demands in cloud environment have been increased incrementally. In order to effectively allocate the resources, the workload prediction of virtual machines (VMs) is a vital issue that makes the VM allocation more instantaneous and reduces the power consumption. In this paper, we propose a workload prediction method using Grey Forecasting model to allocate VMs, which is the first string in the research field. Firstly, we utilize the time-dependent of workload at the same period in every day, and forecast the VM workload tendency towards increasing or decreasing. Next, we compare the predicted value with previous time period on workload usage, then determine to migrate which VM wherein the physical machine (PM) for the balanced workload and lower power consumption. The simulation results show that our proposed method not only uses the fewer data to predict the workload accurately but also allocates the resource of VMs with power saving.

Original languageEnglish
Title of host publicationInternational Conference on Information Networking 2014, ICOIN 2014
PublisherIEEE Computer Society
Pages40-45
Number of pages6
ISBN (Print)9781479936892
DOIs
StatePublished - 2014
Event2014 28th International Conference on Information Networking, ICOIN 2014 - Phuket, Thailand
Duration: 10 Feb 201412 Feb 2014

Publication series

NameInternational Conference on Information Networking
ISSN (Print)1976-7684

Conference

Conference2014 28th International Conference on Information Networking, ICOIN 2014
Country/TerritoryThailand
CityPhuket
Period10/02/1412/02/14

Keywords

  • cloud data center
  • grey interval forecasting
  • power consumption
  • VM migration
  • workload prediction

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