Full-System Power Analysis and Modeling

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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 models to predict full-system power and component power breakdown that are not hardware specific.
To quantitatively understand power consumption trends in server systems, we study the component-level power breakdown and variation, as well as temporal workload-specific power consumption of two instrumented systems: a power-optimized blade system and a compute-optimized Itanium-2 system. Using this analysis, we examine the validity of prior ad-hoc approaches to understanding power breakdown and quantify several interesting trends important for power modeling and management.
We also introduce Mantis, a non-intrusive method for modeling full-system power consumption and providing real-time power prediction. Mantis is not hardware-specific and uses a one-time calibration phase to generate a model correlating AC power measurements with standard user-level system utilization metrics.
We experimentally validate Mantis on the two instrumented systems using a variety of workloads (CPU, memory, or I/O intensive) and hardware configurations (number of processors, power supply and frequency settings). Mantis provides power estimates with high accuracy for both overall and temporal power consumption, making it a valuable non-hardware-specific tool for power-aware scheduling and analysis.


Type of resource text
Date created 2006-05


Author Economou, Dimitris
Advisor Kozyrakis, Christos
Department Stanford University. Department of Electrical Engineering.


Subject Modeling
Subject Information technology industry > Energy conservation
Subject Electric power > Conservation
Genre Thesis

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This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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Economou, Dimitris (2006). Full-System Power Analysis and Modeling. Stanford Digital Repository. Available at http://purl.stanford.edu/wp105cx4248


Undergraduate Theses, School of Engineering

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