Trace-driven modeling and analysis of CPU scheduling in a multiprogramming system

Abstract

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.