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}
}
  • G. Casale
  • Published 5 June 2017
  • Computer Science
  • Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems
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… 
Accelerating Performance Inference over Closed Systems by Asymptotic Methods
  • G. Casale
  • Computer Science
    Proc. ACM Meas. Anal. Comput. Syst.
  • 2017
TLDR
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
TLDR
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.

References

SHOWING 1-10 OF 43 REFERENCES
Accelerating Performance Inference over Closed Systems by Asymptotic Methods
  • G. Casale
  • Computer Science
    Proc. ACM Meas. Anal. Comput. Syst.
  • 2017
TLDR
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.
A Bayesian Approach to Parameter Inference in Queueing Networks
TLDR
A novel iterative approximation of the normalizing constant of the equilibrium state probabilities is defined and the improved accuracy of this approach is shown, compared to existing methods, for use in conjunction with Gibbs sampling.
Monte Carlo summation and integration applied to multiclass queuing networks
TLDR
The application of Monte Carlo summation is proposed in order to determine the normalization constant, throughputs, and gradients of throughputs in closed multiclass queuing networks and a theory for optimal importance sampling is developed.
Auxiliary variables for Bayesian inference in multi-class queueing networks
TLDR
A slice sampling technique with mappings to the measurable space of task transitions between the service stations is introduced that can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks.
Calculating normalization constants of closed queueing networks by numerically inverting their generating functions
TLDR
A new algorithm is developed for calculating normalization constants (partition functions) and moments of product-form steady-state distributions of closed queuing networks and related models that allows the results to be verified in the absence of alternative algorithms.
Bayesian inference for queueing networks and modeling of internet services
TLDR
A Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables is developed and sampled from the posterior distribution over missing data and model parameters using Markov chain Monte Carlo.
An efficient algorithm for the exact analysis of multiclass queueing networks with large population sizes
  • G. Casale
  • Computer Science, Mathematics
    SIGMETRICS '06/Performance '06
  • 2006
TLDR
A novel approach, based on linear systems of equations, which significantly reduces the cost of computing normalizing constants and proposes a block triangular form of the linear system that further reduces the requirements, in terms of both time and storage, of an exact analysis.
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