Datacomp: Locally Independent Adaptive Compression for Real-World Systems

Peter A.H. Peterson, Peter L. Reiher

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

1 Scopus citations

Abstract

Non-lossy compression can save time and energy during communication if the cost to compress and send input is less than the cost of sending it uncompressed. Unfortunately, compression can also degrade performance, no single method is always beneficial, and outcomes depend on many factors. As a result, compression choices in real systems are coarsely grained and manually controlled, resulting in suboptimal or even poor performance. Adaptive Compression (AC) systems make compression choices dynamically to optimize utility. Existing AC systems are limited in ways that reduce their suitability for general-purpose computers. Datacomp is an AC system that operates locally and includes no significant hard-coded knowledge. Using real-world data, a broad range of environments and the Comptool "AC Oracle," we show that Datacomp's performance is equivalent or close to the ideal at bandwidths between 1-100Mbit/s, even when static strategies are suboptimal or more costly than no compression. While Datacomp struggles to perform well at 1Gbit/s, understanding why illustrates important challenges for AC systems and suggests solutions.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages211-220
Number of pages10
ISBN (Electronic)9781509014828
DOIs
StatePublished - Aug 8 2016
Event36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016 - Nara, Japan
Duration: Jun 27 2016Jun 30 2016

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2016-August

Other

Other36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016
CountryJapan
CityNara
Period6/27/166/30/16

Keywords

  • compression
  • efficiency
  • energy
  • optimization

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