Energy efficiency evaluation of distributed systems

James Phung*, Young Choon Lee, Albert Y. Zomaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


Rapid growth in Big Data and Cloud technologies has fueled rising energy demands in large server systems such as data centers, leading to a need for effective power management. In this paper, we investigate the energy consumption characteristics of data-intensive distributed applications in terms of the CPU and memory subsystem. To this end, we develop PowerSave as a lightweight software framework that enables dynamic reconfiguration of power limits. PowerSave uses Running Average Power Limit (RAPL) to impose power limits. Our evaluation study, conducted on three different real systems, demonstrates that for workloads typical of servers used in data centers, higher power caps correlate with higher overall CPU energy use.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2019
Subtitle of host publication19th International Conference, 2019, Proceedings, Part V
EditorsJoão M. F. Rodrigues, Pedro J. S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot
Place of PublicationSwitzerland
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Number of pages8
ISBN (Electronic)9783030227500
ISBN (Print)9783030227494
Publication statusPublished - 1 Jan 2019
Event19th International Conference on Computational Science, ICCS 2019 - Faro, Portugal
Duration: 12 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11540 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computational Science, ICCS 2019


  • Big Data
  • Cloud
  • Energy monitoring
  • Power management
  • RAPL
  • Virtualization


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