Full-System Power Analysis and Modeling for Server Environments

TLDR

Rising power and cooling costs and higher‑density computer systems increase demand for improved power management in servers, yet quantitative understanding of power consumption trends remains limited. The study investigates component‑level power breakdown and temporal workload‑specific consumption in a power‑optimized blade server and introduces Mantis, a nonintrusive method for real‑time full‑system power modeling. Mantis is built by correlating AC power measurements with user‑level utilization metrics during a one‑time calibration, and its accuracy is experimentally validated on a low‑end blade and a high‑end compute‑optimized server across diverse workloads. Mantis provides power estimates with high accuracy for both overall and temporal power consumption, making it a valuable tool for power‑aware scheduling and analysis.

Abstract

The increasing costs of power delivery and cooling, as well as the trend toward higher-density computer systems, have created a growing demand for better power management in server environments. Despite the increasing interest in this issue, little work has been done in quantitatively understanding power consumption trends and developing simple yet accurate models to predict full-system power. We study the component-level power breakdown and variation, as well as temporal workload-specific power consumption of an instrumented power-optimized blade server. Using this analysis, we examine the validity of prior adhoc approaches to understanding power breakdown and quantify several interesting trends important for power modeling and management in the future. We also introduce Mantis, a nonintrusive method for modeling full-system power consumption and providing real-time power prediction. Mantis uses a onetime calibration phase to generate a model by correlating AC power measurements with user-level system utilization metrics. We experimentally validate the model on two server systems with drastically different power footprints and characteristics (a low-end blade and high-end compute-optimized server) using a variety of workloads. Mantis provides power estimates with high accuracy for both overall and temporal power consumption, making it a valuable tool for power-aware scheduling and analysis.