Energy-Efficient, Utility Accrual Scheduling under Resource Constraints for Mobile Embedded Systems

Abstract

We present an energy-efficient, utility accrual, real-time scheduling algorithm called the Resource-constrained Energy-Efficient Utility Accrual Algorithm (or ReUA). ReUA considers an application model where activities are subject to time/utility function (TUF) time constraints, resource dependencies including mutual exclusion constraints, and statistical performance requirements including activity (timeliness) utility bounds that are probabilistically satisfied. Further, ReUA targets mobile embedded systems where system-level energy consumption is also a major concern. For such a model, we consider the scheduling objectives of (1) satisfying the statistical performance requirements; and (2) maximizing the system-level energy efficiency. At the same time, resource dependencies must be respected. Since the problem is NP-hard, ReUA makes resource allocations using statistical properties of application cycle demands and heuristically computes schedules with a polynomial-time cost. We analytically establish several timeliness and non-timeliness properties of the algorithm. Further, our simulation experiments illustrate the algorithm's effectiveness.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA460255

Entities

People

  • Binoy Ravindran
  • E. D. Jensen
  • Haisang Wu
  • Peng Li

Organizations

  • Virginia Tech

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Air Defense
  • Airborne Warning And Control System
  • Algorithms
  • Control Systems
  • Defense Systems
  • Efficiency
  • Embedded Systems
  • Energy Consumption
  • Energy Efficiency
  • Frequency
  • Microarchitecture
  • Probability
  • Probability Distributions
  • Scheduling (Production)
  • Simulations
  • Simulators

Fields of Study

  • Computer science

Readers

  • Energy Conservation and Renewable Energy Engineering.
  • Operations Research
  • Parallel and Distributed Computing.