Modern systems must trade off traditional performance goals with energy concerns, e.g., running faster lowers delays but increases power usage. However, while there are well-developed theories and models for discussing computation, communication, and memory demands of algorithms/systems, a theory for discussing the energy efficiency of an algorithm/system is only developing. In this talk, I will describe some recent work toward this goal, which focuses on dynamic capacity provisioning in data centers. Specifically, I will describe work that investigates (i) how much can be saved by dynamically "right-sizing" the data center through managing the number of active servers and (ii) how "geographical load balancing" can be used to make efficient use of renewable sources in Internet-scale systems. In both contexts I will present our new algorithms, which provide significantly improved performance guarantees when compared with the "standard" approaches using Receding Horizon Control. Additionally, if time allows, I will briefly discuss the our recent progress toward the implementation and evaluation of these algorithms in industry data centers. (The talk includes joint work with Lachlan Andrew, Minghong Lin, Zhenhua Liu, Steven Low, and Eno Thereska.)
Adam Wierman is an Assistant Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he is a member of the Rigorous Systems Research Group (RSRG). He received his Ph.D., M.Sc. and B.Sc. in Computer Science from Carnegie Mellon University in 2007, 2004, and 2001, respectively. His research interests center around resource allocation and scheduling decisions in computer systems and services. More specifically, his work focuses both on developing analytic techniques in stochastic modeling, queueing theory, scheduling theory, and game theory, and applying these techniques to application domains such as energy-efficient computing, data centers, social networks, and the electricity grid.