Heterogeneity aware dominant resource assistant heuristics for virtual machine consolidation

Yan Zhang, Nirwan Ansari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations


Power consumption is a critically important issue for data centers. Virtual machine (VM) consolidation is fundamentally employed to improve resource utilization and power optimization in modern data centers. In general, VM consolidation is formulated as a vector bin packing problem, which is a well-known NP-hard problem. Hence, heuristic algorithms, such as single dimensional heuristics (e.g., first fit decreasing (FFD)) and dimension-aware heuristics (e.g., DotProduct), are usually deployed in practice for VM consolidation. However, all of these previous heuristic algorithms did not sufficiently explore the heterogeneity of the VMs' resource requirements. In this paper, we propose several heterogeneity aware dominant resource assistant heuristic algorithms for VM consolidation. The performance evaluations validate the effects of the proposed heterogeneity aware heuristics on VM consolidation. The proposed heuristics can achieve quite similar consolidation performance as dimension-aware heuristics with almost the same computational cost as those of the single dimensional heuristics.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Communications Conference, GLOBECOM 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781479913534
StatePublished - 2013
Event2013 IEEE Global Communications Conference, GLOBECOM 2013 - Atlanta, GA, United States
Duration: Dec 9 2013Dec 13 2013

Publication series

NameGLOBECOM - IEEE Global Telecommunications Conference


Other2013 IEEE Global Communications Conference, GLOBECOM 2013
Country/TerritoryUnited States
CityAtlanta, GA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Virtual machine consolidation
  • cloud computing
  • data center
  • dominant resource
  • first fit decreasing (FFD)


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