Energy-efficient real-time scheduling of dag tasks

Ashikahmed Bhuiyan, Zhishan Guo, Abusayeed Saifullah, Nan Guan, Haoyi Xiong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


This work studies energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. First, we adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then, we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency. The effectiveness of the proposed approach is evaluated both theoretically via approximation ratio bounds and also experimentally through simulation study. Experimental results on randomly generated workloads show that our algorithms achieve an energy saving of 60% to 68% compared to existing DAG task schedulers.

Original languageEnglish
Article number84
JournalACM Transactions on Embedded Computing Systems
Issue number5
Publication statusPublished - Sept 2018


  • Convex optimization
  • Energy minimization
  • Parallel task
  • Real-time scheduling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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