IEEE Transactions on Computers
Linearizer: a heuristic algorithm for queueing network models of computing systems
Communications of the ACM
Analyzing queueing networks with simultaneous resource possession
Communications of the ACM
IEEE Transactions on Software Engineering
Some Extensions to Multiclass Queueing Network Analysis
Proceedings of the Third International Symposium on Modelling and Performance Evaluation of Computer Systems: Performance of Computer Systems
Journal of Systems and Software
Enhanced Modeling and Solution of Layered Queueing Networks
IEEE Transactions on Software Engineering
A performance experiment system supporting fast mapping of system issues
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
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Layered Queueing Networks (LQN) have been used successfully 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.