Bio-Inspired Heuristics for VM Consolidation in Cloud Data Centers

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

17 Scopus Citations
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Author(s)

  • Jing V. Wang
  • Nuwan Ganganath
  • Chi-Tsun Cheng
  • Chi K. Tse

Detail(s)

Original languageEnglish
Article number8665989
Pages (from-to)152-163
Journal / PublicationIEEE Systems Journal
Volume14
Issue number1
Online published12 Mar 2019
Publication statusPublished - Mar 2020
Externally publishedYes

Abstract

In infrastructure-as-a-service environments, Cloud data centers employ virtualization technology to host various applications in virtual machines (VMs) and enable application isolation on shared physical resources. Additionally, live VM migration has been adopted to perform load balancing by moving VMs across distinct hosts. However, co-located VMs that show significant positive correlations on their CPU utilization patterns are at a higher risk of triggering overloading events and incurring performance degradation, even when their host is operating below its critical limits. To address this problem, a VM consolidation mechanism inspired by host-switching behaviors in symbiotic associates is proposed in this paper. In the proposed mechanism, hosts and VMs in Cloud data centers represent symbionts in an ecosystem. Two heuristic functions, inspired by host susceptibility and symbiotic coefficient among symbionts, are proposed to yield better resource utilization via VM consolidations. Experiment results demonstrate that the proposed mechanism can achieve reductions in both energy consumption and service level agreement violations of Cloud data centers.

Research Area(s)

  • Bio-inspired, heuristics, resource management, utilization correlation, virtual machine (VM) consolidation

Citation Format(s)

Bio-Inspired Heuristics for VM Consolidation in Cloud Data Centers. / Jing V. Wang; Nuwan Ganganath; Chi-Tsun Cheng et al.
In: IEEE Systems Journal, Vol. 14, No. 1, 8665989, 03.2020, p. 152-163.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review