One of the major obstacles to performance evaluation studies is the definition of a representative or accurate workload. User scripts, a detailed description of user activities to specify an interactive workload, can be used as input for remote terminal emulators as well as a representation of an interactive workload the users impose on a system. User scripts are often based on the widely accepted user behaviour model 'LIST-MODS-RUN'. A method of analysis is described which suggests that this user behaviour is not realistic. More accurate models are given to describe user behaviour at system command language level. It is also studied how different users groups adapt to different loads. The results are then used to define a workload model. An automatic workload genrator is described to generate workload models in form of user scripts ready for execution 'on ERTE which is a system designed to exert interactive workloads on a multiaccess system. Experimental runs with ERTE demonstrate the accuracy of the model and provide the basis for more realistic performance measures on EMAS and the development of ERTE.

Creation of sufficiently accurate workload models of computer systems is a key step in evaluating and tuning these systems. Workload models for an observable system can be built from traces collected by observing the system. This dissertation presents a novel technique to construct non-executable, artificial workload models fitting transient trace data. The trace can be a categorical or numerical time-series. The trace is considered a sample realization of a non-stationary stochastic process, {X_t}, such that random variables X_t follow different probability distributions. To estimate the parameters for the model a Rate Evolution Graph (REG) is built from the trace data. The REG is a two-dimensional Cartesian graph which plots the number of occurrences of each unique state in the trace on the ordinate and time on the abscissa. The REG contains one path for all instances of each unique state in the trace. The derivative of a REG path at time t is used as an estimate of the probability of occurrence of the corresponding state at t. We use piecewise linear regression to fit straight line segments to each REG path. The slopes of the line segments that fit a REG path estimate the time dependent probability of occurrence of the corresponding state. The estimates of occurrence probabilities of all unique states in the trace are used to construct a time-dependent joint probability mass function. The joint probability mass function is the representation of the Pzrecewise Independent Stochastic Process model for the trace. Two methods that assist to compact the model, while retaining accuracy, are also discussed. / Ph. D.