Techniques for energy-efficient power budgeting in data centers

Abstract

We propose techniques for power budgeting in data centers, where a large power budget is allocated among the servers and the cooling units such that the aggregate performance of the entire center is maximized. Maximizing the performance for a given power budget automatically maximizes the energy efficiency. We first propose a method to partition the total power budget among the cooling and computing units in a self-consistent way, where the cooling power is sufficient to extract the heat of the computing power. Given the computing power budget, we devise an optimal computing budgeting technique based on knapsack-solving algorithms to determine the power caps for the individual servers. The optimal computing budgeting technique leverages a proposed on-line throughput predictor based on performance counter measurements to estimate the change in throughput of heterogeneous workloads as a function of allocated server power caps. We set up a simulation environment for a data center, where we simulate the air flow and heat transfer within the center using computational fluid dynamic simulations to derive accurate cooling estimates. The power estimates for the servers are derived from measurements on a real server executing heterogeneous workload sets. Our budgeting method delivers good improvements over previous power budgeting techniques.