IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T19:03:22ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2016-04-13T09:40:21Z2016-04-13T09:40:21Zhttp://eprints.imtlucca.it/id/eprint/3444This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/34442016-04-13T09:40:21ZScaling Size and Parameter Spaces in Variability-Aware Software Performance Models (T)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.Matthias KowalMax Tschaikowskimax.tschaikowski@imtlucca.itMirco Tribastonemirco.tribastone@imtlucca.itIna Schaefer2015-12-03T13:32:51Z2015-12-03T13:32:51Zhttp://eprints.imtlucca.it/id/eprint/2961This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/29612015-12-03T13:32:51ZModel-based Development and Performance Analysis for Evolving Manufacturing SystemsManufacturing systems and their control software exhibit a large number of variants, which evolve over time in order to meet changing functional and non-functional requirements. To handle the resulting complexity, we propose a multi-perspective modeling approach with different viewpoints regarding workflow, architecture and component behavior. We combine it with delta modeling to seamlessly capture variability and evolution by the same means on each of the viewpoints. We show how the separation in different viewpoints enables early performance analysis as well as code generation. The approach is illustrated using a case study.Matthias KowalChristian PrehoferIna SchaeferMirco Tribastonemirco.tribastone@imtlucca.it2015-02-10T15:06:34Z2015-02-10T15:06:34Zhttp://eprints.imtlucca.it/id/eprint/2592This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/25922015-02-10T15:06:34ZFamily-based performance analysis of variant-rich software systemsWe study models of software systems with variants that stem from a specific choice of configuration parameters with a direct impact on performance properties. Using UML activity diagrams with quantitative annotations, we model such systems as a product line. The efficiency of a product-based evaluation is typically low because each product must be analyzed in isolation, making difficult the re-use of computations across variants. Here, we propose a family-based approach based on symbolic computation. A numerical assessment on large activity diagrams shows that this approach can be up to three orders of magnitude faster than product-based analysis in large models, thus enabling computationally efficient explorations of large parameter spaces.Matthias KowalIna SchaeferMirco Tribastonemirco.tribastone@imtlucca.it