Exploiting Non-deterministic Analysis in the Integration of Transient Solution Techniques for Markov Regenerative Processes

Biagi, Marco; Carnevali, Laura; Paolieri, Marco; Papini, Tommaso; Vicario, Enrico

doi:10.1007/978-3-319-66335-7_2

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…The software architecture, designed to implement new features of Petri models and new analysis methods, makes ORIS also a flexible research tool to evaluate novel solutions for discrete-event systems. Over the years, ORIS has been successfully used in a variety of contexts and application domains, for instance as a GUI to evaluate performability measures in railway signaling systems [18], as an API to perform sensitivity analysis of maintenance procedures in gas distribution networks [21], and also as a tool to implement a new solution technique for transient analysis of non-Markovian models [10].…”

Section: Discussionmentioning

confidence: 99%

The ORIS Tool: Quantitative Evaluation of Non-Markovian Systems

Paolieri

Biagi

Carnevali

et al. 2021

IIEEE Trans. Software Eng.

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We present the next generation of ORIS, a toolbox for quantitative evaluation of concurrent models with non-Markovian timers. The tool shifts its focus from timed models to stochastic ones, it includes a new graphical user interface, new analysis methods and a Java Application Programming Interface (API). Models can be specified as Stochastic Time Petri Nets (STPNs) through the graphical editor, validated using an interactive token game, and analyzed through several techniques to compute instantaneous or cumulative rewards. STPNs can also be exported as Java code to conduct extensive parametric studies through the Java library, now distributed as open-source. A well-engineered software architecture allows the user to implement new features for STPNs, new modeling formalisms, and new analysis methods. The most distinctive features of ORIS include transient and steady-state analysis of STPNs modeling Markov Regenerative Processes (MRPs), and transient analysis of STPNs modeling generalized semi-Markov processes. ORIS also supports state-space analysis of Time Petri Nets (TPNs), simulation of STPNs, and standard analysis techniques for continuous-time Markov chains or MRPs with at most one non-exponential timer in each state. We illustrate the general workflow for the application of ORIS to the modeling and evaluation of non-functional requirements of software-intensive systems.

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Section: Discussionmentioning

confidence: 99%

The ORIS Tool: Quantitative Evaluation of Non-Markovian Systems

Paolieri

Biagi

Carnevali

et al. 2021

IIEEE Trans. Software Eng.

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“…Efficient numerical solution techniques can still be applied with GEN durations if the model guarantees that a new regeneration point is always reached with probability 1 [19], [20], i.e., a time instant where the Markov condition is satisfied, so that future behavior depends on the past only through the discrete logical location. In this case, the underlying stochastic process of the model is a Markov Regenerative Process (MRP), which can be solved with relative efficiency under various restrictions, the most notable being the already mentioned enabling restriction [12], and the bounded regeneration restriction [9], i.e., the number of steps between subsequent regeneration points is bounded.…”

Section: ç 1 Introductionmentioning

confidence: 99%

Compositional Analysis of Hierarchical UML Statecharts

Carnevali¹,

German

Santoni

et al. 2022

IIEEE Trans. Software Eng.

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Quantitative evaluation of stochastic models supports early verification of design choices and assessment of non-functional requirements. Model Driven Engineering (MDE) leverages automated derivation of formal stochastic models from semi-formal artifacts of the Unified Modeling Language (UML) to facilitate deployment of quantitative evaluation methods without disrupting industrial practices. As a major limitation, when generally distributed (GEN) temporal parameters are considered to enhance the model expressivity, the structure and complexity of the underlying stochastic process cannot be easily controlled, possibly impairing the model analyzability. We present a hierarchical modeling formalism based on UML statecharts with GEN durations, designed to guarantee ease of modeling and efficient evaluation of steady-state or transient behaviour until absorption. To this end, fairly lax restrictions are applied to the model syntax to enable separate analysis of the Semi-Markov Process (SMP) underlying each model component. Scalability of solution is assessed by analyzing a suite of synthetic models referred to the context of timed Failure Logic Analysis (FLA) of component-based systems, specifically designed to point out each factor of computational complexity. Notably, the analysis derives both the probability that the system is in each step before failure and the Cumulative Distribution Function (CDF) of the duration of the overall failure process. A challenging case study that significantly and jointly stresses the main factors of computational complexity is finally addressed, performing steady-state analysis of a non-Markovian variant of a server virtualized system from the literature on software rejuvenation.

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