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Issue No.01 - Jan. (2014 vol.25)
pp: 200-211
Yuan Yao , University of Southern California, Los Angeles
Longbo Huang , University of California, Berkeley, Berkeley
Abhishek B. Sharma , NEC labs America, Princeton
Leana Golubchik , University of Southern California, Los Angeles
Michael J. Neely , University of Southern California, Los Angeles
ABSTRACT
This paper considers a stochastic optimization approach for job scheduling and server management in large-scale, geographically distributed data centers. Randomly arriving jobs are routed to a choice of servers. The number of active servers depends on server activation decisions that are updated at a slow time scale, and the service rates of the servers are controlled by power scaling decisions that are made at a faster time scale. We develop a two-time-scale decision strategy that offers provable power cost and delay guarantees. The performance and robustness of the approach is illustrated through simulations.
INDEX TERMS
Servers, Delay, Optimization, Distributed databases, Algorithm design and analysis, Vectors, Routing,performance analysis, Power management, data center, stochastic optimization
CITATION
Yuan Yao, Longbo Huang, Abhishek B. Sharma, Leana Golubchik, Michael J. Neely, "Power Cost Reduction in Distributed Data Centers: A Two-Time-Scale Approach for Delay Tolerant Workloads", IEEE Transactions on Parallel & Distributed Systems, vol.25, no. 1, pp. 200-211, Jan. 2014, doi:10.1109/TPDS.2012.341
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