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Solving layered queueing networks of large client-server systems with symmetric replication
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Source Workshop on Software and Performance archive
Proceedings of the 5th international workshop on Software and performance table of contents
Palma, Illes Balears, Spain
Pages: 159 - 166  
Year of Publication: 2005
ISBN:1-59593-087-6
Authors
Tariq Omari  Carleton University, Ottawa, ON Canada
Greg Franks  Carleton University, Ottawa, ON Canada
Murray Woodside  Carleton University, Ottawa, ON Canada
Amy Pan  Carleton University, Ottawa, ON Canada
Sponsors
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 7,   Downloads (12 Months): 62,   Citation Count: 1
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ABSTRACT

Large distributed client-server systems often contain subsystems which are either identical to each other, or very nearly so, and this simplifies the system description for planning purposes. These replicated components and subsystems all have the same workload and performance parameters. It is known how to exploit this symmetry to simplify the solution of some kinds of performance models, using state aggregation in Markov Chains. This work considers the same problem for layered queueing models, using mean value analysis. The mean values are found for each group of replicas just once, and then are inserted appropriately into the solution of the system as a whole. An algorithm has been implemented in the Layered Queueing Network Solver (LQNS), including approximations to deal with interactions among the replicas, and is evaluated for accuracy and for efficiency. The resulting solver is insensitive (in time of solution) to the number of replicas in a group, and can efficiently calculate waiting times and throughputs for systems with tens of thousands of nodes and processes.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Tariq Omari: colleagues
Greg Franks: colleagues
Murray Woodside: colleagues
Amy Pan: colleagues