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Cluster Computing

, Volume 22, Issue 3, pp 757–782 | Cite as

Power-network aware VM migration heuristics for multi-tier web applications

  • Amir Hossein BorhaniEmail author
  • Terence Hung
  • Bu-Sung Lee
  • Zheng Qin
Article
  • 56 Downloads

Abstract

Cloud computing has become an attractive and promising platform, offering on-demand resources for multi-tier web applications. However, an inappropriate and inefficient resource management practices may negatively affect the service level agreement (SLA) and the response time experienced by users, essentially for high load operating conditions. Furthermore, this may result in substantial amount of energy consumption in data centers, which consequently leads to a high operational cost. This paper proposes an effective power-network aware virtual machine (VM) migration heuristics to deal with high SLA violation (SLAV) and energy consumption. Our research consists of two parts. The first part introduces a network-aware VM migration algorithm. The algorithm considers steady-state traffic condition to minimize the negative effect of migration on other flows. The network gain (NG) is calculated for candidate VMs and the VM with the maximum NG is selected. The second part, extends the network-aware algorithm with energy-awareness capabilities. In addition to NG, power gain (PG) is calculated for each candidate VM and two lists are created for each congested link: NG list and PG list. The VM with the lowest sum of the rank is selected. An extensive simulation is done in CloudSim. The results show that the power-network aware algorithm can reduce the energy consumption without significant increase in SLAV. This research enables us to take a step further towards building low latency, energy-efficient and environment-friendly data centers running network intensive applications.

Keywords

Resource management Energy efficiency and management Service level agreement Multi-tier web applications Network-aware VM migration Energy-aware VM migration 

Notes

Acknowledgements

This research was granted by Agency for Science, Technology and Research (A*Star) of Singapore, under A*STAR Thematic Programme: User and Domain Driven Data Analytics as a Service framework (SERC 1021580034) Grant. The authors would like to acknowledge Associate Professor Anis Yazidi of the Department of Computer Science, University College of Oslo and Akershus of Applied Sciences, Oslo, Norway, who kindly gave advice and help.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Amir Hossein Borhani
    • 1
    Email author
  • Terence Hung
    • 2
  • Bu-Sung Lee
    • 1
  • Zheng Qin
    • 3
  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Rolls-Royce Pte LtdSingaporeSingapore
  3. 3.Institute of High-Performance Computing (IHPC)SingaporeSingapore

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