Relating layered queueing networks and process algebra models

Tribastone, Mirco

doi:10.1145/1712605.1712634

Cited by 15 publications

(22 citation statements)

References 22 publications

(26 reference statements)

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“…A promising line of research seems to be, in the context of PEPA, the use of theoretical error probability bounds of densitydependent chains [15]. For a numerical investigation into the quality of the approximation of stiff large-scale models, the interested reader is referred to [16].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The increase in the population counts of P and Q by the same value is expressed with the same rate being present in dx 2 (t)/dt and dx 4 (t)/dt, but with opposite sign. The other summands in (17) may be obtained from analogous interpretations of the generating functions (15) and (16). A family of CTMCs {X n (t)} can be systematically associated with a PEPA model by taking a density vector, denoted by δ ∈ N d 0 , which is interpreted as giving the relative proportions between the distinct sequential components.…”

Section: Motivating Examplementioning

confidence: 99%

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Scalable Differential Analysis of Process Algebra Models

Tribastone

Gilmore

Hillston

2012

IIEEE Trans. Software Eng.

Self Cite

118

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Abstract-The exact performance analysis of large-scale software systems with discrete-state approaches is difficult because of the well-known problem of state-space explosion. This paper considers this problem with regard to the stochastic process algebra PEPA, presenting a deterministic approximation to the underlying Markov chain model based on ordinary differential equations. The accuracy of the approximation is assessed by means of a substantial case study of a distributed multi-threaded application.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Motivating Examplementioning

confidence: 99%

Scalable Differential Analysis of Process Algebra Models

Tribastone

Gilmore

Hillston

2012

IIEEE Trans. Software Eng.

Self Cite

118

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“…Tribastone [10] compared the solutions of LQNS and the numerical solution of the Markov chain underlying an PEPA model for a distributed system. He found a prediction error below 15 percent in most of the analyzed cases and showed that the LQN solver was orders of magnitude faster in cases with low concurrency levels.…”

Section: Quantitative Comparisonsmentioning

confidence: 99%

“…Quantitative comparisons of model transformations and/or analysis tools can be found in [9], [10], [11]. However, Balsamo et al [9] do not compare the respective solver execution times, and the comparison conducted by Tribastone et al [10], [11] does not involve model transformations as the stochastic performance models are modeled by hand.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-Based Architectures

Brosig

Meier²,

Becker

et al. 2015

IIEEE Trans. Software Eng.

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Abstract-During the last decade, researchers have proposed a number of model transformations enabling performance predictions. These transformations map performance-annotated software architecture models into stochastic models solved by means of analytical or numerical analysis or by system simulation. However, so far, a detailed quantitative evaluation of the accuracy and efficiency of different transformations is missing, making it hard to select an adequate transformation for a given context. This paper provides an in-depth comparison and quantitative evaluation of representative model transformations to, e.g., Queueing Petri Nets and Layered Queueing Networks. The semantic gaps between typical source model abstractions and the different analysis techniques are revealed. The accuracy and efficiency of each transformation are evaluated by considering four case studies representing systems of different size and complexity. The presented results and insights gained from the evaluation help software architects and performance engineers to select the appropriate transformation for a given context, thus significantly improving the usability of model transformations for performance prediction.

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“…) See for instance [40], [38] for a study of these results in software performance models expressed in a process algebra [23], and [35], [36] for an application to layered queuing networks [21].…”

Section: Definition 3 (Ode Semantics) the Ode Semantics Of A Paad (Vmentioning

confidence: 99%

Scaling Size and Parameter Spaces in Variability-Aware Software Performance Models (T)

Kowal

Tschaikowski

Tribastone

et al. 2015

2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Abstract-In software performance engineering, what-if scenarios, architecture optimization, capacity planning, run-time adaptation, and uncertainty management of realistic models typically require the evaluation of many instances. Effective analysis is however hindered by two orthogonal sources of complexity. The first is the infamous problem of state space explosion-the analysis of a single model becomes intractable with its size. The second is due to massive parameter spaces to be explored, but such that computations cannot be reused across model instances. In this paper, we efficiently analyze many queuing models with the distinctive feature of more accurately capturing variability and uncertainty of execution rates by incorporating general (i.e., non-exponential) distributions. Applying product-line engineering methods, we consider a family of models generated by a core that evolves into concrete instances by applying simple delta operations affecting both the topology and the model's parameters. State explosion is tackled by turning to a scalable approximation based on ordinary differential equations. The entire model space is analyzed in a family-based fashion, i.e., at once using an efficient symbolic solution of a super-model that subsumes every concrete instance. Extensive numerical tests show that this is orders of magnitude faster than a naive instance-by-instance analysis.

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Relating layered queueing networks and process algebra models

Cited by 15 publications

References 22 publications

Scalable Differential Analysis of Process Algebra Models

Scalable Differential Analysis of Process Algebra Models

Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-Based Architectures

Scaling Size and Parameter Spaces in Variability-Aware Software Performance Models (T)

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