期刊
IEEE TRANSACTIONS ON COMPUTERS
卷 63, 期 1, 页码 45-58出版社
IEEE COMPUTER SOC
DOI: 10.1109/TC.2013.122
关键词
Load distribution; multicore server processor; power allocation; queuing model; response time
资金
- Ministry of Science and Technology of China [2011CB302805, 2013CB228206, 2011CB302505]
- National Natural Science Foundation of China [61233016]
- Academic Exchange Foundation of Tsinghua National Laboratory for Information Science and Technology
- Government of China
- NSERC Canada Discovery grant
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.
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