A computer program to predict the performance of local area networks based on an Ethernet or other medium is described. The effects of bursty traffic are modeled as packet streams which are initially bulk Poisson, but whose clusters spread apart as they propagate through the network. It is shown by example that performance predictions using more conventional Poisson assumptions tend to be optimistic, often extremely so. The program presents a friendly, dialogue-oriented user interface, including a library of rather sophisticated transaction types for network loading. A simulator is included in the program to validate its analytical approximations, and two example simulations are presented. Although approximation errors can be large in highly bursty situations, these errors appear to be much less than those which would be obtained by treating bursty traffic as if it were Poisson.

To the class of queuing networks analyzable by the method of Baskett, Chandy, Muntz, and Palacios, we add service centers whose scheduling is random. That is, upon completion of a service interval, the server chooses next to serve one of the waiting customers selected at random. As in the case of first-come first-served (FCFS) scheduling, all tasks must have the same exponentially distributed service time at such a center. We show that for purposes of this analysis, the results are identical to FCFS queuing. Example applications for random selection scheduling in computer system modeling are provided.

A class of schedules in the two customer central server queueing model, consisting of a “CPU” server and m “I/O” servers, is considered. Optimal (maximal CPU utilization) CPU and I/O schedules are obtained. The best CPU schedule depends on the I/O schedule in effect; and is either Longest or Shortest-Expected-Remaining-Processing-Time-First. However, for certain I/O schedules the CPU schedule is immaterial. The best I/O schedule is always to process the (expected) longer CPU customer first.

A model of paged multiprogramming computer systems using variable storage partitioning is considered. A variable storage partitioning policy is one which allocates storage among the active tasks according to a sequence of fixed partitions of main storage. The basic result obtained is, mean processing efficiency is increased and mean fault-rate decreased under a variable partition, provided that the curves of efficiency and fault-rate as a function of allocated space are concave up.