Memory energy management for an enterprise decision support system

Abstract

Energy efficiency is an important factor in designing and configuring enterprise servers. In these servers, memory may consume 40% of the total system power. Different memory configurations (sizes, numbers of ranks, speeds, etc.) can have significant impacts on the performance and energy consumption of enterprise workloads. Many of these workloads, such as decision support systems (DSS), require large amounts of memory. This paper investigates the potential to save energy by making the memory configuration adaptive to workload behavior. We present a case study on how memory configurations affect energy consumption and performance for running DSS. We measure the energy consumption and performance of a commercial enterprise server, and develop a model to describe the conditions when energy can be saved with acceptable performance degradation. Using this model, we identify opportunities to save energy in future enterprise servers.