TY - JOUR
T1 - Analysis of Power Consumption in Heterogeneous Virtual
Machine Environments
AU - Negru, Catalin
AU - Monacu, Mariana
AU - Cristea, Valentin
AU - Sotiriadis, Stelios
AU - Bessis, Nik
PY - 2017/8/1
Y1 - 2017/8/1
N2 - 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.
AB - 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.
KW - Cloud computing
KW - Data intensive-applications
KW - Energy-efficiency
KW - Virtualization
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U2 - 10.1007/s00500-016-2129-7
DO - 10.1007/s00500-016-2129-7
M3 - Article (journal)
SN - 1432-7643
VL - 21
SP - 4531
EP - 4542
JO - Soft Computing - A Fusion of Foundations, Methodologies and Applications
JF - Soft Computing - A Fusion of Foundations, Methodologies and Applications
IS - 16
ER -