VM scaling based on Hurst exponent and Markov transition with empirical cloud data

Chien Tung Lu, Chia Wei Chang, Jung Shian Li

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

One of the major benefits of cloud computing is virtualization scaling. Compared to existing studies on virtual machine scaling, this paper introduces Hurst exponent which gives additional characteristics for data trends to supplement the often used Markov transition approach. This approach captures both the long and short-term behaviors of the virtual machines (VMs). The dataset for testing of this approach was gathered from the computer usage of key servers supporting a large university. Performance evaluation shows our approach can assist prediction of VM CPU usage toward effective resource allocation. In turn, this allows the cloud resource provider to monitor and allocate the resource usage of all VMs in order to meet the service level agreements for each VM client.

Original languageEnglish
Pages (from-to)199-207
Number of pages9
JournalJournal of Systems and Software
Volume99
DOIs
Publication statusPublished - 2015 Jan 1

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture

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