A Queue-Shift Approximation Technique for Product-Form Queueing Networks

Abstract

Increasing complexity of actual computer systems has made exact modelling techniques prohibitively expensive, and the need for approximation techniques for performance evaluation is well-recognized. A new approximation technique is given for product-form queueing networks with constant-rate servers. It estimates the shift in mean queue lengths rather than the fractional deviations used in the Chandy-Neuse linearizer. Experimental results are reported which show that the new approximation 92% of the times has superior accuracy to linearizer. As population grows, the superior accuracy over linearizer increases. In 58% of the test cases, the new approximation technique gave errors of zero (at least 6 digits) while linearizer achieves such accuracy in less than 2.5% of cases. In some of the stress cases described, the new approximation technique has roughly five orders of magnitude less error than linearizer.

This work was partially supported by the Center for Manufacturing and Operations Management of the W. E. Simon Graduate School of Business Administration at the University of Rochester

This work was partially supported by MURST 40% Project and CESTIA-CNR Italy