Feedback EDF Scheduling of Real-Time Tasks Exploiting Dynamic Voltage Scaling

  • Authors:
  • Yifan Zhu;Frank Mueller

  • Affiliations:
  • Department of Computer Science/Center for Embedded Systems Research, North Carolina State University, Raleigh, USA 27695-7534;Department of Computer Science/Center for Embedded Systems Research, North Carolina State University, Raleigh, USA 27695-7534

  • Venue:
  • Real-Time Systems
  • Year:
  • 2005

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Abstract

Many embedded systems are constrained by limits on power consumption, which are reflected in the design and implementation for conserving their energy utilization. Dynamic voltage scaling (DVS) has become a promising method for embedded systems to exploit multiple voltage and frequency levels and to prolong their battery life. However, pure DVS techniques do not perform well for systems with dynamic workloads where the job execution times vary significantly. In this paper, we present a novel approach combining feedback control with DVS schemes targeting hard real-time systems with dynamic workloads. Our method relies strictly on operating system support by integrating a DVS scheduler and a feedback controller within the earliest-deadline-first (EDF) scheduling algorithm. Each task is divided into two portions. The objective within the first portion is to exploit frequency scaling for the average execution time. Static and dynamic slack is accumulated for each task with slack-passing and preemption handling schemes. The objective within the second portion is to meet the hard real-time deadline requirements up to the worst-case execution time following a last-chance approach. Feedback control techniques make the system capable of selecting the right frequency and voltage settings for the first portion, as well as guaranteeing hard real-time requirements for the overall task. A feedback control model is given to describe our feedback DVS scheduler, which is used to analyze the system's stability. Simulation experiments demonstrate the ability of our algorithm to save up to 29% more energy than previous work for task sets with different dynamic workload characteristics.