Energy-Efficient Parallel Real-Time Scheduling on Clustered Multi-Core

Ashikahmed Bhuiyan, Di Liu, Aamir Khan, Abusayeed Saifullah, Nan Guan, Zhishan Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Energy-efficiency is a critical requirement for computation-intensive real-time applications on multi-core embedded systems. Multi-core processors enable intra-task parallelism, and in this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware real-time scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 67 percent through our proposed approach compared to existing methods.

Original languageEnglish
Article number9066868
Pages (from-to)2097-2111
Number of pages15
JournalIEEE Transactions on Parallel and Distributed Systems
Volume31
Issue number9
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • cluster-based platform
  • energy minimization
  • heterogeneous platform
  • Parallel task
  • real-time scheduling

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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