Analysis of Power Consumption in Heterogeneous Virtual Machine Environments

Catalin Negru, Mariana Monacu, Valentin Cristea, Stelios Sotiriadis, Nik Bessis

Research output: Contribution to journalArticle (journal)peer-review

15 Citations (Scopus)
73 Downloads (Pure)


Reduction of energy consumption in Cloud computing datacenters today is a hot a research topic, as these consume large amounts of energy. Further more, most of the energy is used inefficiently because of the improper usage of computational resources such as CPU, storage, and network. A good balance between the computing resources and performed workload is mandatory. In the context of data-intensive applications, a significant portion of energy is consumed just to keep alive virtual machines or to move data around without performing useful computation. Moreover, heterogeneity of resources increases the degree of difficulty, when try to achieve energy efficiency. Power consumption optimization requires identification of those inefficiencies in the underlying system and applications. Based on the relation between server load and energy consumption, we study the efficiency of data-intensive applications, and the penalties, in terms of power consumption, that are introduced by different degrees of heterogeneity of the virtual machines characteristics in a cluster.
Original languageEnglish
Pages (from-to)4531-4542
Number of pages12
JournalSoft Computing - A Fusion of Foundations, Methodologies and Applications
Issue number16
Early online date28 Apr 2016
Publication statusPublished - 1 Aug 2017


  • Cloud computing
  • Data intensive-applications
  • Energy-efficiency
  • Virtualization


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