Power Consumption Estimation Models for Processors, Virtual Machines, and Servers

• Published in: IEEE transactions on parallel and distributed systems Vol. 25; no. 6; pp. 1600 - 1614
• Main Authors: Mobius, Christoph, Dargie, Waltenegus, Schill, Alexander
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmark testing; Data centers; Electric potential; energy-efficiency; Estimates; Estimation; Measurement techniques; Power consumption; power consumption estimation; Power consumption models; Power demand; Power measurement; processor's power consumption; Processors; Program processors; Radiation detectors; server's power consumption; Servers; Task scheduling; Virtual environments; virtual machine's power consumption
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2013.183

The power consumption of presently available Internet servers and data centers is not proportional to the work they accomplish. The scientific community is attempting to address this problem in a number of ways, for example, by employing dynamic voltage and frequency scaling, selectively switching off idle or underutilized servers, and employing energy-aware task scheduling. Central to these approaches is the accurate estimation of the power consumption of the various subsystems of a server, particularly, the processor. We distinguish between power consumption measurement techniques and power consumption estimation models. The techniques refer to the art of instrumenting a system to measure its actual power consumption whereas the estimation models deal with indirect evidences (such as information pertaining to CPU utilization or events captured by hardware performance counters) to reason about the power consumption of a system under consideration. The paper provides a comprehensive survey of existing or proposed approaches to estimate the power consumption of single-core as well as multicore processors, virtual machines, and an entire server.