Efficient synthesis of robust models for stochastic systems

Călinescu, Radu; Češka, Milan; Gerasimou, Simos; Kwiatkowska, Marta; Paoletti, Nicola

doi:10.1016/j.jss.2018.05.013

Cited by 51 publications

(47 citation statements)

References 19 publications

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“…Since its introduction in Daws' seminal work [20] in 2004, parametric model checking has underpinned the development of a vast array of methods for the modelling and analysis of software and other computer-based systems. These include methods for comparing alternative system designs [30], [31], sensitivity analysis [26], parameter synthesis [8], [9], [22], [32], probabilistic model repair [3], [16], dynamic reconfiguration of self-adaptive systems [14], [21], [24], [25], and synthesis of confidence intervals for the QoS properties of software systems [10], [11]. These methods address very different problems, and yet most researchers who developed them mention the same limitation of parametric model checking: its computationally intensive nature.…”

Section: Related Workmentioning

confidence: 99%

Efficient Parametric Model Checking Using Domain Knowledge

Călinescu

Paterson

Johnson

2021

IIEEE Trans. Software Eng.

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We introduce an efficient parametric model checking (ePMC) method for the analysis of reliability, performance and other quality-of-service (QoS) properties of software systems. ePMC speeds up the analysis of parametric Markov chains modelling the behaviour of software by exploiting domain-specific modelling patterns for the software components. To this end, ePMC precomputes closed-form expressions for key QoS properties of such patterns, and uses these expressions in the analysis of whole-system models.To evaluate ePMC, we show that its application to service-based systems and multi-tier software architectures reduces analysis time by several orders of magnitude compared to current parametric model checking methods.

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Section: Related Workmentioning

confidence: 99%

Efficient Parametric Model Checking Using Domain Knowledge

Călinescu

Paterson

Johnson

2021

IIEEE Trans. Software Eng.

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“…Ideally, these techniques and tools would start from a set of SCPS requirements, and would present developers with alternative systems designs that satisfy the requirements and are Pareto-optimal with respect to multiple optimisation criteria such as cost, utility and environmental impact. Such solutions have been proposed recently for the development of software systems (e.g., [24], [25]), and can also handle uncertainty in the operational profile of the system under development [26], [27]. However, these solutions cannot yet handle some of the new types of requirements encountered in SCPS.…”

Section: Oc2) Ensuring the Accuracy Of Stochastic Models Of Scps-mentioning

confidence: 99%

Socio-Cyber-Physical Systems: Models, Opportunities, Open Challenges

Călinescu¹,

Cámara

Paterson³

2019

2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS)

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Almost without exception, cyber-physical systems operate alongside, for the benefit of, and supported by humans. Unsurprisingly, disregarding their social aspects during development and operation renders these systems ineffective. In this paper, we explore approaches to modelling and reasoning about the human involvement in socio-cyber-physical systems (SCPS). To provide an unbiased perspective, we describe both the opportunities afforded by the presence of human agents, and the challenges associated with ensuring that their modelling is sufficiently accurate to support decision making during SCPS development and, if applicable, at run-time. Using SCPS examples from emergency management and assisted living, we illustrate how recent advances in stochastic modelling, analysis and synthesis can be used to exploit human observations about the impact of natural and man-made disasters, and to support the efficient provision of assistive care.

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“…This extension is presented in Section 4.2, implemented by our new OMNI tool described in Section 5, and shown to reduce verification times by 54-74% (compared to the preliminary OMNI version) in Section 6.5. This is a particularly significant improvement because the same QoS property is often verified many times, to identify suitable values for the parameters of the modelled system (e.g., see the case studies from [32]- [36]). Additionally, we considerably extended and improved the validation of OMNI by evaluating it for the following two systems: 1) A service-based system that we implemented using six real-world web services -two commercial web services provided by Thales Group, three free Bing web services provided by Microsoft, and a free WebserviceX.Net web service.…”

Section: Introductionmentioning

confidence: 99%

Observation-Enhanced QoS Analysis of Component-Based Systems

Paterson

Călinescu

2020

IIEEE Trans. Software Eng.

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Abstract-We present a new method for the accurate analysis of the quality-of-service (QoS) properties of component-based systems. Our method takes as input a QoS property of interest and a high-level continuous-time Markov chain (CTMC) model of the analysed system, and refines this CTMC based on observations of the execution times of the system components. The refined CTMC can then be analysed with existing probabilistic model checkers to accurately predict the value of the QoS property. The paper describes the theoretical foundation underlying this model refinement, the tool we developed to automate it, and two case studies that apply our QoS analysis method to a service-based system implemented using public web services and to an IT support system at a large university, respectively. Our experiments show that traditional CTMC-based QoS analysis can produce highly inaccurate results and may lead to invalid engineering and business decisions. In contrast, our new method reduced QoS analysis errors by 84.4-89.6% for the service-based system and by 94.7-97% for the IT support system, significantly lowering the risk of such invalid decisions.Index Terms-Quality of service, component-based systems, Markov models, probabilistic model checking.

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Efficient synthesis of robust models for stochastic systems

Cited by 51 publications

References 19 publications

Efficient Parametric Model Checking Using Domain Knowledge

Efficient Parametric Model Checking Using Domain Knowledge

Socio-Cyber-Physical Systems: Models, Opportunities, Open Challenges

Observation-Enhanced QoS Analysis of Component-Based Systems

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