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Power management in multi-server data centers~especially at scale is a vitalissue of increasing importance in cloud computing paradigm. Existing studiesmostly consider thresholds on the number of idle servers to switch the serverson or off and suffer from scalability issues. As a natural approach inview~of~the Markovian assumption, we present a multi-level continuous-timeMarkov decision process (CTMDP) model based on state aggregation ofmulti-server data centers with setup times that interestingly overcomes theinherent intractability of traditional MDP approaches due to their colossalstate-action space. The beauty of the presented model is that, while it keepsloyalty to the Markovian behavior, it approximates the calculation of thetransition probabilities in a way that keeps the accuracy of the results at adesirable level. Moreover, near-optimal performance is attained at the expenseof the increased state-space dimensionality by tuning the number of levels inthe multi-level approach. The simulation results were promising and confirmthat in many scenarios of interest, the proposed approach attains noticeableimprovements, namely a near 50% reduction in the size of CTMDP while yieldingbetter rewards as compared to existing fixed threshold-based policies andaggregation methods.

Scaling Power Management in Cloud Data Centers: A Multi-Level Continuous-Time MDP Approach