TAPA: Temperature aware power allocation in data center with Map-Reduce

Shen Li, Tarek Abdelzaher, Mindi Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we analytically derive, implement, and empirically evaluate a solution for maximizing the execution rate of Map-Reduce jobs subject to power constraints in data centers. Our solution is novel in that it takes into account the dependence of power consumption on temperature, attributed to temperature-induced changes in leakage current and fan speed. While this dependence is well-known, we are the first to consider it in the context of maximizing the throughput of Map-Reduce workdloads. Accordingly, we provide a new power model and optimization strategy for temperature-aware power allocation (TAPA), and modify Hadoop on a 13-machine cluster to implement our optimization algorithm. Our experimental results show that TAPA can not only limit the power consumption to the power budget but also achieves higher computational efficiency against static solutions and temperature oblivious DVFS solutions.

Original languageEnglish (US)
Title of host publication2011 International Green Computing Conference and Workshops, IGCC 2011
DOIs
StatePublished - Sep 30 2011
Event2011 International Green Computing Conference, IGCC 2011 - Orlando, FL, United States
Duration: Jul 25 2011Jul 28 2011

Publication series

Name2011 International Green Computing Conference and Workshops, IGCC 2011

Other

Other2011 International Green Computing Conference, IGCC 2011
Country/TerritoryUnited States
CityOrlando, FL
Period7/25/117/28/11

Keywords

  • DVFS
  • data center
  • energy management
  • map-reduce
  • thermal-aware optimization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Environmental Engineering

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