Approximation algorithm for periodic real-time tasks with workload-dependent running-time functions

  • Authors:

  • D. Juedes;F. Drews;D. Gu;L. Welch;K. Ecker;S. Schomann

  • Affiliations:

  • Center for Intelligent, Distributed and Dependable Systems, School of Electrical Engineering & Computer Science, Ohio University Athens, USA 45701;Center for Intelligent, Distributed and Dependable Systems, School of Electrical Engineering & Computer Science, Ohio University Athens, USA 45701;Center for Intelligent, Distributed and Dependable Systems, School of Electrical Engineering & Computer Science, Ohio University Athens, USA 45701;Center for Intelligent, Distributed and Dependable Systems, School of Electrical Engineering & Computer Science, Ohio University Athens, USA 45701;Department of Computer Science, Clausthal University of Technology, Germany;Department of Computer Science, Clausthal University of Technology, Germany

  • Venue:
  • Real-Time Systems
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper addresses the problem of resource allocation for distributed real-time periodic tasks, operating in environments that undergo unpredictable changes and that defy the specification of meaningful worst-case execution times. These tasks are supplied by input data originating from various environmental workload sources. Rather than using worst-case execution times (WCETs) to describe the CPU usage of the tasks, we assume here that execution profiles are given to describe the running time of the tasks in terms of the size of the input data of each workload source. The objective of resource allocation is to produce an initial allocation that is robust against fluctuations in the environmental parameters. We try to maximize the input size (workload) that can be handled by the system, and hence to delay possible (costly) reallocations as long as possible. We present an approximation algorithm based on first-fit and binary search that we call FFBS. As we show here, the first-fit algorithm produces solutions that are often close to optimal. In particular, we show analytically that FFBS is guaranteed to produce a solution that is at least 41% of optimal, asymptotically, under certain reasonable restrictions on the running times of tasks in the system. Moreover, we show that if at most 12% of the system utilization is consumed by input independent tasks (e.g., constant time tasks), then FFBS is guaranteed to produce a solution that is at least 33% of optimal, asymptotically. Moreover, we present simulations to compare FFBS approximation algorithm with a set of standard (local search) heuristics such as hill-climbing, simulated annealing, and random search. The results suggest that FFBS, in combination with other local improvement strategies, may be a reasonable approach for resource allocation in dynamic real-time systems.