Cooling-Aware Energy and Workload Management in Data Centers via Stochastic Optimization

While the quest of end users for fast and convenient Internet services grows steadily, energy-hungry data centers correspondingly expand in both numbers and scale - a fact that raises global warming and climate change concerns. In addition, high penetration of renewables, development of energy-efficient cooling facilities, and flexibility of distributed storage units, all call for a system-wide energy and workload management policy for future sustainable data centers. As implementing offline management policies is practically infeasible due to complexity and the lack of future information, real-time management schemes are considered here under a systematic framework. Leveraging stochastic optimization tools, a unified management approach is proposed allowing data centers to adaptively respond to intermittent availability of renewables, variability of cooling efficiency, information technology (IT) workload shift, and energy price fluctuations under long-term quality-of-service (QoS) requirements. Meanwhile, it is rigorously established that when storage devices have sufficiently high capacity, or, the difference between electricity purchase and selling prices is small, the proposed algorithm yields a feasible and near-optimal management strategy without knowing the distributions of the independently and identically distributed (i.i.d.) workload, renewable, and electricity price processes. Numerical results further demonstrate that the proposed algorithm works well not only for i.i.d. processes, but also in real-data scenarios, where the underlying randomness is highly correlated over time.