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Precise Scheduling of DAG Tasks with Dynamic Power Management

Authors Ashikahmed Bhuiyan , Mohammad Pivezhandi, Zhishan Guo , Jing Li , Venkata Prashant Modekurthy, Abusayeed Saifullah

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Author Details

Ashikahmed Bhuiyan
  • Department of Computer Science, West Chester University, PA, USA
Mohammad Pivezhandi
  • Department of Computer Science, Wayne State University, Detroit, MI, USA
Zhishan Guo
  • Department of Computer Science, North Carolina State University, Raleigh, NC, USA
Jing Li
  • Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
Venkata Prashant Modekurthy
  • Department of Computer Science, University of Nevada, Las Vegas, NV, USA
Abusayeed Saifullah
  • Department of Computer Science, Wayne State University, Detroit, MI,USA

Funding

  • Bhuiyan, Ashikahmed: Provost’s Research Grant, West Cheser University
  • Guo, Zhishan: NSF CCF-2028481, Startup Grant at NC State University
  • Li, Jing: NSF CNS-1948457
  • Modekurthy, Venkata Prashant: UNLV Troesh Center, CNS-2211640
  • Saifullah, Abusayeed: CNS-2301757, CAREER-2306486, CNS-2306745

Cite As Get BibTex

Ashikahmed Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Prashant Modekurthy, and Abusayeed Saifullah. Precise Scheduling of DAG Tasks with Dynamic Power Management. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 8:1-8:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ECRTS.2023.8

Abstract

The rigid timing requirement of real-time applications biases the analysis to focus on the worst-case performances. Such a focus cannot provide enough information to optimize the system’s typical resource and energy consumption. In this work, we study the real-time scheduling of parallel tasks on a multi-speed heterogeneous platform while minimizing their typical-case CPU energy consumption. Dynamic power management (DPM) policy is integrated to determine the minimum number of cores required for each task while guaranteeing worst-case execution requirements (under all circumstances). A Hungarian Algorithm-based task partitioning technique is proposed for clustered multi-core platforms, where all cores within the same cluster run at the same speed at any time, while different clusters may run at different speeds. To our knowledge, this is the first work aiming to minimize typical-case CPU energy consumption (while ensuring the worst-case timing correctness for all tasks under any execution condition) through DPM for parallel tasks in a clustered platform. We demonstrate the effectiveness of the proposed approach with existing power management techniques using experimental results and simulations. The experimental results conducted on the Intel Xeon 2680 v3 12-core platform show around 7%-30% additional energy savings.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time system architecture
Keywords
  • Parallel task
  • mixed-criticality scheduling
  • energy minimization
  • dynamic power management
  • cluster-based platform

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