Energy efficient real-time task scheduling on CPU-GPU hybrid clusters

Xinxin Mei, Xiaowen CHU, Hai Liu, Yiu Wing LEUNG, Zongpeng Li

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

51 Citations (Scopus)


Conserving the energy consumption of large data centers is of critical significance, where a few percent in consumption reduction translates into millions-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a sequence of real-time tasks under deadline constraints. We compute the appropriate voltage/frequency setting for each task through mathematical optimization, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 38% of energy can be saved, we record 30-36% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption.

Original languageEnglish
Title of host publicationINFOCOM 2017 - IEEE Conference on Computer Communications
ISBN (Electronic)9781509053360
Publication statusPublished - May 2017
Event2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States
Duration: 1 May 20174 May 2017

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference2017 IEEE Conference on Computer Communications, INFOCOM 2017
Country/TerritoryUnited States

Scopus Subject Areas

  • Computer Science(all)
  • Electrical and Electronic Engineering


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