The ORIS Tool: Quantitative Evaluation of Non-Markovian Systems

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

doi:10.1109/tse.2019.2917202

Cited by 23 publications

(6 citation statements)

References 69 publications

(131 reference statements)

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“…In future work, by considering different costs and execution times for each action, it would be possible to consider the applied action sequence cost as another optimization objective. Our next goal is to investigate a stochastic distributed system by using the Markov Decision Petri Nets [44] in ORIS [45] as the underlying stochastic system to design a more practical synthetic environment.…”

Section: Discussionmentioning

confidence: 99%

Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities

Zadenoori

Vicario

2022

Sensors

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The operation of a variety of natural or man-made systems subject to uncertainty is maintained within a range of safe behavior through run-time sensing of the system state and control actions selected according to some strategy. When the system is observed from an external perspective, the control strategy may not be known and it should rather be reconstructed by joint observation of the applied control actions and the corresponding evolution of the system state. This is largely hurdled by limitations in the sensing of the system state and different levels of noise. We address the problem of optimal selection of control actions for a stochastic system with unknown dynamics operating under a controller with unknown strategy, for which we can observe trajectories made of the sequence of control actions and noisy observations of the system state which are labeled by the exact value of some reward functions. To this end, we present an approach to train an Input–Output Hidden Markov Model (IO-HMM) as the generative stochastic model that describes the state dynamics of a POMDP by the application of a novel optimization objective adopted from the literate. The learning task is hurdled by two restrictions: the only available sensed data are the limited number of trajectories of applied actions, noisy observations of the system state, and system state; and, the high failure costs prevent interaction with the online environment, preventing exploratory testing. Traditionally, stochastic generative models have been used to learn the underlying system dynamics and select appropriate actions in the defined task. However, current state of the art techniques, in which the state dynamics of the POMDP is first learned and then strategies are optimized over it, frequently fail because the model that best fits the data may not be well suited for controlling. By using the aforementioned optimization objective, we try to to tackle the problems related to model mis-specification. The proposed methodology is illustrated in a scenario of failure avoidance for a multi component system. The quality of the decision making is evaluated by using the collected reward on the test data and compared against the previous literature usual approach.

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Section: Discussionmentioning

confidence: 99%

Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities

Zadenoori

Vicario

2022

Sensors

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“…STPN's are a graphical model and offer great convenience to a modeler in arriving at a credible, high-level model of a system. The analysis of STPN is supported by the ORIS Tool [8,29], which efficiently implements the method of stochastic state classes, including regenerative transient, regenerative steady-state, and non-deterministic analyses.…”

Section: Stochastic Timed Petri Netsmentioning

confidence: 99%

Future Train Control Systems: Challenges for Dependability Assessment

Fantechi

Gnesi

Gori

2022

Lecture Notes in Computer Science

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The prospected advent of advanced train control systems, such as moving block and virtual coupling, raises the issue of the effects that uncertainty on critical parameters (such as position or speed) can have on dependability. Several approaches to the evaluation of such effects have been proposed, typically based on a state-based formal modelling of the system behaviour. We present a survey of such proposals.

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“…A replication artifact is also available at https://doi.org/10.5281/zenodo.5574241. PYRA-MIS uses the SIRIO library [77] of the ORIS tool [69] to represent expolynomial distributions, to perform DTMC steady-state analysis, and to compare results with regenerative analysis [39], [62], the only other analytical approach for quantitative evaluation of models that include multiple concurrent non-Markovian timers possibly with firmly bounded support. Experiments are performed on a single core of an Intel(R) Xeon(R) Gold 5120 CPU 2.20 GHz with 32.0 GB RAM.…”

Section: Q3 Tradeoff Accuracy Versus Complexitymentioning

confidence: 99%

“…Statecharts with GEN durations and event probabilities are mapped in [44] to stochastic input/output automata, enabling quality of service evaluation through model checking. In [10], a Stochastic Time Petri Net (STPN) model is derived from an activity diagram with GEN durations, and the resulting underlying stochastic process is analyzed through forward transient analysis based on the implementation of the method of stochastic state classes [18], [39], [84] provided by the SIRIO library of the ORIS tool [69].…”

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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The ORIS Tool: Quantitative Evaluation of Non-Markovian Systems

Cited by 23 publications

References 69 publications

Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities

Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities

Future Train Control Systems: Challenges for Dependability Assessment

Compositional Analysis of Hierarchical UML Statecharts

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