Performance Evaluation of Stochastic Timed Decision-Free Petri Nets,

Abstract

The advent of low-cost processors has made the construction of highly complex, decentralized systems feasible. These systems are usually divided into interacting components which operate concurrency and coordinate their operations by using an asynchronous protocol. Petri Nets have frequently been used to model such systems they can model concurrency and asynchronous protocols. Specific examples of systems models by Petri Nets include computer systems communication protocols and manufacturing systems. This paper shows how the performance of a simple subclass of STNPs, that of Stochastic Timed Decision-Free Petri Nets (STDFPNs), can be predicted. This subclass can model concurrency and coordination, but not decisions, and have been used to model certain manufacturing systems. The major contribution is a new analysis technique which uses a set of equations that describe the behavior of that system. The resulting algorithms are computationally more attractive and the general approach can be extended to study less restrictive classes of SRPNs. This paper examines three related processes which describe firing times, relative firing times, and intertransition times. The first process introduces the new analytical technique, the second one provides convergence proof, and the third one allows performance measures of interest to be computed.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1985
Accession Number
ADA153987

Entities

People

  • R. P. Wiley

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Circuits
  • Computer Science
  • Computers
  • Equations
  • Equations Of State
  • Equivalent Circuits
  • Firing Rate
  • Markov Chains
  • Markov Processes
  • Multithreading
  • Petri Nets
  • Probability
  • Probability Distributions
  • Random Variables
  • Steady State
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Parallel and Distributed Computing.