PowerPrep: A power management proposal for user-facing datacenter workloads

Abstract

Modern data center applications are user facing/latency critical. Our work analyzes the characteristics of such applications i.e., high idleness, unpredictable CPU usage, and high sensitivity to CPU performance. In spite of such execution characteristics, datacenter operators disable sleep states to optimize performance. Deep-sleep states hurt performance mainly due to: a) high wake-latency and b) cache warm-up after exiting deep-sleep. To address these challenges, we quantify three necessary characteristics required to realize deep-sleep states in datacenter applications: a) low wake-latency, b) low resident power, and c) selective retention of cache-state. Using these observations, we show how emerging technological advances can be leveraged to improve the energy efficiency of latency-critical datacenter workloads.