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This paper presents a computationally tractable methodology for including accurately the effects of finite memory size and workload memory requirements in queueing network models of computer systems. Empirical analyses and analytic studies based on applying this methodology to an actual multiaccess interactive system are reported. Relations between workload variables such as memory requirement distribution and job swap time, and performance measures such as response time and memory utilization are graphically displayed. A multiphase, analytically soluble model is proposed as being broadly applicable to the analysis of interactive computer systems which use nonpaged memories. [ABSTRACT FROM AUTHOR]

Microscopic level Job stream data obtained in a production environment by an event-driven software probe is used to drive a model of a multiprogramming computer system. The CPU scheduling algorithm of the model is systematically varied. This technique, called trace-driven modeling, provides an accurate replica of a production environment for the testing of variations in the system. At the same time alterations in scheduling methods can be easily carried out in a controlled way with cause and effects relationships being isolated. The scheduling methods tested included the best possible and worst possible methods, the traditional methods of multiprogramming theory, round-robin, first-come- first-served, etc., and dynamic predictors. The relative and absolute performances of these scheduling methods are given. It is concluded that a successful CPU scheduling method must be preemptive and must prevent a given job from holding the CPU for too long a period. [ABSTRACT FROM AUTHOR]