Compositional Analysis of Expected Delay in Networks of Automata

Abstract

We have succeeded in extending our earlier work on probabilistic I/O automata to obtain a practical method for computing termination probabilities and expected termination times in networks of PIOA. Our method is compositional, avoiding the construction of the network's global state space, and as a result very efficient. This represents the culmination of work we first reported in last year's annual report. Probabilistic I/O Automata (PIOA), a natural extension of the '10 Automaton model of Lynch and Tuttle, have been developed by the Pis to model and analyze distributed systems that exhibit behavior that is statistical or probabilistic in nature and subject to timing constraints. The objectives of this grant are to: (1) continue our investigation of the probabilistic I/O automron model with an emphasis on developing techniques that will permit the probabilistic behavior and delay in distributed systems to be analyzed compositionally; (2) implement the resulting techniques in the Concurrency Factory, a CASE environment for the design, specification, verification, and implementation of asynchronous distributed systems; and 3. perform a technology transfer with Northrop Grumman by applying our techniques to several applications currently of interest to Northrop Grumman.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2000
Accession Number
ADA388763

Entities

People

  • Scott A. Smolka

Organizations

  • Stony Brook University

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Automata
  • Command And Control
  • Communications Protocols
  • Complex Systems
  • Computer Science
  • Computers
  • Construction
  • Local Area Networks
  • Models
  • Network Protocols
  • Probability
  • Probability Distributions
  • Random Variables
  • Specifications
  • Technology Transfer
  • Theoretical Computer Science

Fields of Study

  • Computer science

Readers

  • Mathematical Modeling and Probability Theory.
  • Software Engineering

Technology Areas

  • Space