- Author:
- Douglas Malcolm Neuse
Four new algorithms for the approximate analysis of large and general queueing networks are presented. Three of the algorithms allow accurate, iterative analysis of closed, product-form networks with many large-population job classes and many queues, including queues with load-dependent service rates. These algorithms are simple and require very little memory; the memory requirements are independent of job population. The fourth algorithm allows the accurate analysis of closed networks with multiple job classes and arbitrary numbers of queues violating product-form requirements. A systematic procedure for testing heuristic queueing network algorithms and a method for simplifying the description and analysis of Markov models are presented.
Cited By
- Pattipati K and Kostreva M On the properties of approximate mean value analysis algorithms for queueing networks Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems, (244-252)
- Pattipati K and Kostreva M (1988). On the properties of approximate mean value analysis algorithms for queueing networks, ACM SIGMETRICS Performance Evaluation Review, 16:1, (244-252), Online publication date: 1-May-1988.
- Conway A and Georganas N (2018). RECAL—a new efficient algorithm for the exact analysis of multiple-chain closed queuing networks, Journal of the ACM (JACM), 33:4, (768-791), Online publication date: 10-Aug-1986.
- Anderson G (1984). The coordinated use of five performance evaluation methodologies, Communications of the ACM, 27:2, (119-125), Online publication date: 1-Feb-1984.
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