Energy Efficiency of Cloud Virtual Machines: From Traffic Pattern and CPU Affinity Perspectives

Chi Xu, Ziyang Zhao, Haiyang Wang, Ryan Shea, Jiangchuan Liu

Research output: Contribution to journalReview article

4 Scopus citations

Abstract

Networking and machine virtualization play critical roles in the success of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past, including the impact from network traffic. When it comes to virtual machines (VMs) in cloud data centers, the interplay between energy consumption and network traffic, however, becomes much more complicated. Through real-world measurement on both Xen- and KVM-based platforms, we show that these state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling network transactions, generating excessive interrupt requests with ceaseless context switching, which in turn, increases energy consumption. Even when a physical machine is in an idle state, its VM's network transactions will incur nontrivial energy consumption. More interestingly, the energy consumption significantly varies with traffic allocation strategies and virtual CPU affinity conditions, which was not seen in conventional physical machines. Looking closely into the virtualization architectures, we then pinpoint the root causes and examine that our measurement results can be extended for various network configurations. Moreover, we also provide initial solutions toward optimizing energy consumption in virtualized environments.

Original languageEnglish (US)
Article number7134723
Pages (from-to)835-845
Number of pages11
JournalIEEE Systems Journal
Volume11
Issue number2
DOIs
StatePublished - Jun 2017

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Keywords

  • Cloud computing
  • energy consumption
  • measurement
  • networking
  • virtualization

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