Passage Time Distribution for a Class of Queueing Networks: Closed, Open, or Mixed, with Different Classes of Customers with Applications to Computer System Modeling.

Abstract

Networks of queues are important models of multiprogrammed time-shared computer systems and computer communication networks. Although equilibrium state probabilities of a broad class of network models have been derived in the past, analytic or approximate solutions for response time distributions or more general passage time distribution are still open problems. In this paper the passage time problem is formulated as a 'hitting time' or 'first passage time' problem in a Markov system and derive the analytic solution to passage time distributions of closed queueing networks. Efficient numerical approximation is also proposed. The result for closed queueing networks is further extended to obtain approximate passage time distributions for open queueing networks. Finally, we employ the techniques derived in this paper to study the interfault time and response time distribution and density functions of multiprogrammed computer systems. The effects of program behavior, degree of multiprogramming, size of main memory, service time of paging devices and rate of file I/O requests on the shape of distribution functions and density functions have been examined.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1977
Accession Number
ADA042723

Entities

People

  • Philip S. Yu

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Ballistic Missiles
  • Central Processing Units
  • Classification
  • Communication Networks
  • Computer Communications
  • Computer Programming
  • Computer Science
  • Computers
  • Defense Systems
  • Distribution Functions
  • Engineering
  • Linear Accelerators
  • Markov Chains
  • Probability
  • Simulations
  • Steady State
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Computer Science.
  • Mathematical Modeling and Probability Theory.