A hierarchical demand response framework for data center power cost optimization under real-world electricity pricing

We study the problem of optimizing data center electric utility bill under uncertainty in workloads and real-world pricing schemes. Our focus is on using control knobs that modulate the power consumption of IT equipment. To overcome the difficulty of casting/updating such control problems and the computational intractability they suffer from in general, we propose and evaluate a hierarchical optimization framework wherein an upper layer uses (i) temporal aggregation to restrict the number of decision instants during a billing cycle to computationally feasible values, and (ii) spatial (i.e., control knob) aggregation whereby it models the large and diverse set of power control knobs with two abstract knobs labeled demand dropping and demand delaying. These abstract knobs operate upon a fluid power demand. The key insight underlying our modeling is that the power modulation effects of most IT control knobs can be succinctly captured as dropping and/or delaying a portion of the power demand. These decisions are passed onto a lower layer that leverages existing research to translate them into decisions for real IT knobs. We develop a suite of algorithms for our upper layer that deal with different forms of input uncertainty. An experimental evaluation of the proposed approach offers promising results: e.g., it offers net cost savings of about 25% and 18% to a streaming media server and a MapReduce-based batch workload, respectively.