Specialising Guava’s Cache to Reduce Energy Consumption

  • Nathan Burles
  • Edward Bowles
  • Bobby R. Bruce
  • Komsan Srivisut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9275)

Abstract

In this article we use a Genetic Algorithm to perform parameter tuning on Google Guava’s Cache library, specialising it to OpenTripPlanner. A new tool, Opacitor, is used to deterministically measure the energy consumed, and we find that the energy consumption of OpenTripPlanner may be significantly reduced by tuning the default parameters of Guava’s Cache library. Finally we use Jalen, which uses time and CPU utilisation as a proxy to calculate energy consumption, to corroborate these results.

Keywords

Parameter tuning Library specialisation Energy profiling Reduced power consumption 

Notes

Acknowledgement

Work funded by UK EPSRC grant EP/J017515/1. Data available at https://github.com/nburles/burles2015specialising.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nathan Burles
    • 1
  • Edward Bowles
    • 1
  • Bobby R. Bruce
    • 2
  • Komsan Srivisut
    • 1
  1. 1.University of YorkYorkUK
  2. 2.CREST CentreUniversity College LondonLondonUK

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