2026-07-22T16:59:07Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:7167 2024-03-12T11:58:34Z ddc:004 open_access

Energy-Efficient Multi-Core Scheduling for Real-Time DAG Tasks Guo, Zhishan Bhuiyan, Ashikahmed Saifullah, Abusayeed Guan, Nan Xiong, Haoyi Parallel task Real-time scheduling Energy minimization Convex optimization 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Zhishan Guo and Ashikahmed Bhuiyan and Abusayeed Saifullah and Nan Guan and Haoyi Xiong 2017 Is Part Of LIPIcs, Volume 76, 29th Euromicro Conference on Real-Time Systems (ECRTS 2017) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ECRTS.2017.22 urn:nbn:de:0030-drops-71675 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2017.22 eng https://creativecommons.org/licenses/by/3.0/legalcode