A Mean Value Analysis (MVA) approximation is presented for computing the average performance measures of closed-, open-, and mixed-type multiclass queuing networks containing Preemptive Resume (PR) and nonpreemptive Head-Of-Line (HOL) priority service centers.The approximation has essentially the same storage and computational requirements as MVA, thus allowing computationally efficient solutions of large priority queuing networks.The accuracy of the MVA approximation is systematically investigated and presented.It is shown 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 shown to be superior to that of Sevcik's shadow approximation.

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.

Multiresource queuing systems are of particular maportance in modding compnter systems because a job must have access to a processor and main memory sunultaneousIy in order to proceed.Existing methods of determining maximum processing rates for multiresourcequeuing systems are limited to small memory sizes because problem compleraty grows exponentially with increasing memory size.By restneting our attention to a particular scheduling discipline (first-come-first-loaded or FCFL) and treating memory as the limiting resource, methods of calculating maximum processing ~ates of memory bound systems for reahstic mare memory sizes are derived.The distribution of the number of jobs loaded under the FCFL pohey is given m terms of a convolution of the memory request size distribution.The time averaged behavior of the number of loaded jobs is also found.Finally, the framework is extended to allow multiple job classes in the input stream.The results of fins approach allow one to estimate main memory size requirements from a workload characterization given m terms of arrival rate, memory size distribution, and CPU service rate.

Given a collection of distributed programs and the modules they use, the module allocation problem is to determine an assignment of modules to processors that minimizes the total execution cost of the programs. Standard approaches to this problem are based on solving either a network flow problem or a constrained 0-1 integer programming problem.

Operational analysis replaces certain classical queueing theory assumptions with the conditions of "homogeneous service times" and "on-line= off-line behavior." In this paper we explore the relationship between the operational and classical concepts for the sample paths of an M/G/1 queueing system. The primary results are that the sample paths can have these operational properties with nonzero probability if and only if the service time is exponential. We also state dual results for interarrival times in G/M/l. Additionally, we show that open, feedforward networks of single server queues can have product form solutions valid across a range of system arrival rates if and only if all of the service times are exponential. Finally, we consider the relationship between the operational quantities S(n) and the mean service time in M/G/1. This relationship is shown to depend on the form of the service time distribution. It follows that using operational analysis to predict the performance of an M/G/1 queueing system will be most successful when the service time is exponential. Simulation evidence is presented which supports this claim.

Queueing theoretic models of single and multi-processor computer systems have received wide attention in the computer science literature. Few of these models consider the effect of finite memory size of a machine and its impact on the memory scheduling problem. In an effort to formulate an analytical model for memory scheduling we propose four simple models and examine their characteristics using simulation. In this paper, we discuss some interesting results of these simulations.