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Title: Representative queueing network models of computer systems in terms of time delay probability distributions
Author: Harrison, P. G.
ISNI:       0000 0001 3539 5469
Awarding Body: University of London
Current Institution: Imperial College London
Date of Award: 1979
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In order to obtain a good representation of Computer Systems for performance evaluation, conventional analytic models require improvement from two points of view. First there has been a tendency to concentrate on known analy-tic results and their extensions, obtaining representation of a specific system by choice of model parameter. values. It is argued here that a tnuty nepne.aentatLve model is best achieved by studying the properties of the real system first, and then determining the appropriate model type and structure from them. Secondly, the most crucial performance measures for both manage-ment and users, are the time delays that relate to the rate at which individual tasks are being processed. Conventional models predict only overall resource utilisations and queue lengths. Much of this thesis is concerned with distributions of time delays in queueing networks. An approximate method for their deter-mination is presented which is applicable to a very general class of networks and gives an efficient implementation. Exact results are then derived for cycle time distribution, first in cyclic and then in more general tree-like networks. Validation of both methods is by comparison with simulated results, sufficiently detailed data from real systems being unavailable. Subject to adequate precision, approximate methods are, in gen-eral, more feasible as tools because of their greater generality and superior efficiency. We view and apply the exact method as a stand-ard by which to assess the accuracy of various approximations whilst also recognising its potential as a practical tool for simple cases. Finally, the thesis addresses the almost universal assumption of "equilibrium", that is the assumption that the state space prob-ability distribution is time independent. The time periods over Which this assumption can or should not be made are quantified via time-dependent analysis that is applicable to a very general class of networks and relevant in many transient situations.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available