Accelerating Performance Inference over Closed Systems by Asymptotic Methods
@article{Casale2017AcceleratingPI, title={Accelerating Performance Inference over Closed Systems by Asymptotic Methods}, author={Giuliano Casale}, journal={Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems}, year={2017} }
Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enterprise applications for automated management and performance analysis. In spite of this trend, even simple performance inference problems involving queueing theoretic formulas often incur computational bottlenecks, for example upon computing likelihoods in models of batch systems. Motivated by this issue, we revisit the solution of multiclass closed queueing networks, which are popular models used…
3 Citations
Accelerating Performance Inference over Closed Systems by Asymptotic Methods
- Computer ScienceProc. ACM Meas. Anal. Comput. Syst.
- 2017
This work proves that the normalizing constant of the equilibrium state probabilities of a closed model can be reformulated exactly as a multidimensional integral over the unit simplex, and derives a method based on cubature rules to efficiently evaluate the proposed integral form in small and medium-sized models.
Automated Multi-paradigm Analysis of Extended and Layered Queueing Models with LINE
- Computer ScienceICPE Companion
- 2019
An object-oriented modeling language aligned with the abstraction of the Java Modelling Tools (JMT) simulator and a set of native solvers based on state-of-the-art analytical and simulation-based solution paradigms are introduced.
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