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Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

Cao, Junwei, Li, Keqin and Stojmenovic, Ivan 2014, Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers, IEEE transactions on computers, vol. 63, no. 1, pp. 45-58, doi: 10.1109/TC.2013.122.

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Title Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers
Author(s) Cao, Junwei
Li, Keqin
Stojmenovic, Ivan
Journal name IEEE transactions on computers
Volume number 63
Issue number 1
Start page 45
End page 58
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2014-01
ISSN 0018-9340
Keyword(s) load distribution
multicore server processor
power allocation
queuing model
response time
Summary For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.
Language eng
DOI 10.1109/TC.2013.122
Field of Research 080110 Simulation and Modelling
0803 Computer Software
0805 Distributed Computing
1006 Computer Hardware
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30073178

Document type: Journal Article
Collection: School of Information Technology
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Created: Fri, 28 Aug 2015, 13:19:07 EST

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