A Multiclass Queueing Network model (MQN) is partitioned into a set of disjoint subnetworks. Population constraints are applied to each subnetwork such that within each subnetwork each population chain is either subject to an individual population constraint, or a group of chains may be subject to a common (shared) population constraint. Such population constraints are necessary in order to model multiprogramming level constraints in mainframe computer systems and window flow control mechanisms in computer communication networks. A computationally efficient approximate solution method is developed for solving MQN's with population constraints. Each subnetwork is reduced to a single approximately flow equivalent composite centre by assuming that the effect of other chains on a given chain can be adequately represented by their average customer populations. The accuracy of the population constraint approximation is compared against previous techniques by applying it to a set of test cases for which simulation solutions have previously been reported. The accuracy of the approximation technique is found to be good and in general is an improvement over previously published concurrency constraint approximations.

A Mean Value Analysis (MVA) approximation is presented for computing the average performance measures of closed multiclass queueing networks containing non pre-emptive Head Of Line (HOL) and Pre-empt Resume (PR) priority centers. The approximation has the same storage and computational requirements as MVA thus allowing computationally efficient solutions of large priority queueing networks. The accuracy of the MVA PR approximation is systematically investigated and presented in terms of error contour diagrams. The contour diagrams reveal that the approximation can compute the average performance measures of priority networks to within an accuracy of 5 percent for a large range of network parameter values. Accuracy of the method is also compared to Sevcik's shadow approximation and another MVA approximation recently proposed by Chandy and Lakshmi.

A queuing analysis of a preemptive, priority-driven, time-sliced dispatcher algorithm, typical of those found in many time-sharing systems, is presented. A distinctive feature of the system being modeled is that a preempted task, when readmitted to the dispatcher, is reallocated a full time slice rather than the residual. The combination of preemption, time-slice renewal and class-dependent arrival and service rates places the analysis beyond that of the well-known FBN algorithms. The model is used to predict the behavior of the dispatcher algorithm under various workloads. The model reveals that, at high arrival rates, the time-slice renewal policy prevents processor bound tasks from being relegated to the lower priority queues, thereby severely degrading the throughput of tasks with short processor requirements.

Multiclass queuing network models of multiprogramming computer systems are frequently used to predict the performance of computing systems as a function of user workload and hardware configuration. This paper examines three different methods for incorporating operating system overhead in multiclass queuing network models. The goal of the resultant model is to provide an accurate account of the processing performance and the system CPU overhead of each of the several different types of jobs (batch, timesharing, transaction processing, etc.) that together make up the multiprogramming workload. The first method introduces an operating sysbtm workload consisting of a fixed number of jobs to represent system CPU overhead processing. The second method extends the jobs' CPU service requests to include explicitly the CPU overhead necessary for system processing. The third method employs a communicating set of user and system job classes so that the CPU overhead can be modeled by switching jobs from user to system class whenever they require system CPU service. The capabilities and accuracy of the three methods are assessed and compared against performance and overhead data measured on a Univac 1110 computer.