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

Chien Tung Lu, Chia Wei Chang, Jung Shian Li

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)199-207
頁數9
期刊Journal of Systems and Software
99
DOIs
出版狀態Published - 2015 一月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 資訊系統
  • 硬體和架構

指紋

深入研究「VM scaling based on Hurst exponent and Markov transition with empirical cloud data」主題。共同形成了獨特的指紋。

引用此