Approximating response time distributions
SIGMETRICS '89 Proceedings of the 1989 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Throughput calculation for basic stochastic rendezvous networks
Performance Evaluation
Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
Static performance prediction of data-dependent programs
Proceedings of the 2nd international workshop on Software and performance
Response time densities in generalised stochastic petri net models
WOSP '02 Proceedings of the 3rd international workshop on Software and performance
IEEE Transactions on Software Engineering
Performance analysis of distributed server systems
Performance analysis of distributed server systems
Efficient approximation of response time densities and quantiles in stochastic models
WOSP '04 Proceedings of the 4th international workshop on Software and performance
Future Generation Computer Systems - Systems performance analysis and evaluation
Deriving distribution of thread service time in layered queueing networks
WOSP '07 Proceedings of the 6th international workshop on Software and performance
Rule-based automatic software performance diagnosis and improvement
Performance Evaluation
Rule-based automatic software performance diagnosis and improvement
Performance Evaluation
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Quality of service requirements are normally given in terms of soft deadlines, such as "90% of responses should complete within one second". To estimate the probability of meeting the target delay, one must estimate the distribution of response time, or at least its tail. Exact analytic methods based on state-space analysis suffer from state explosion, and simulation, which is also feasible, is very time consuming. Rapid approximate estimation would be valuable, especially for those cases which do not demand great precision, and which require the exploration of many alternative models.This work adapts layered queueing analysis, which is highly scalable and provides variance estimates as well as mean values, to estimate soft deadline success rates. It evaluates the use of an approximate Gamma distribution fitted to the mean and variance, and its application to examples of software systems. The evaluation finds that, for a definable set of situations, the tail probabilities over 90% are estimated well within a margin of 1% accuracy, which is useful for practical purposes.