Efficiency improvements for solving layered queueing networks

Layered Queueing Networks (LQN) have been used success-fully by numerous researchers to solve performance models of multi-tier client server systems. A common approach for solving a LQN is to split the model up into a set of submodels, then employ approximate mean value analysis (AMVA) on each of these submodels in an interactive fashion and using the results from the solution of one submodel as inputs to the others. This paper addresses the performance of the layered queueing network solver, LQNS, in terms of submodel construction and in terms of changes to Bard-Schweitzer and Linearizer AMVA, in order to improve performance. In some of the models described in this paper, there is a difference in four orders of magnitude between the fastest and slowest approaches. Copyright 2012 ACM