The MVA Priority Approximation
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.
"© ACM, 1984. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Computer Systems (TOCS), 2, 4, November 1984 http://doi.acm.org/10.1145/357401.357406" Received June 1983; revised July 1984; accepted July 1984. The global balance solver used to calculate the exact solutions was written by Bryan Rosenburg of the University of Wisconsin-Madison. This program was an essential part of the research reported here. Dinkar Sitaram, also of the University of Wisconsin-Madison, provided valuable assistance during the early phase of testing of the MVA algorithms reported here.