Energy-efficient multi-core scheduling for real-time DAG tasks

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

37 Citations (Scopus)


In this work, we study 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 the best of 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. We first 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 and to prove the bound of the approximation ratio. We perform a simulation study to demonstrate the effectiveness and efficiency of the proposed scheduling. The simulation results show that our algorithms achieve an energy saving of 27% to 41% compared to existing DAG task schedulers.
Original languageEnglish
Title of host publication29th Euromicro Conference on Real-Time Systems, ECRTS 2017
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Number of pages2001
ISBN (Electronic)9783959770378
Publication statusPublished - 1 Jun 2017
Event29th Euromicro Conference on Real-Time Systems, ECRTS 2017 - Dubrovnik, Croatia
Duration: 28 Jun 201730 Jun 2017


Conference29th Euromicro Conference on Real-Time Systems, ECRTS 2017


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

ASJC Scopus subject areas

  • Software


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