Performance evaluation of stochastic real-time systems with the SBIP framework

Nouri, Ayoub; Mediouni, Braham Lotfi; Bozga, Marius; Combaz, Jacques; Bensalem, Saddek; Legay, Axel

doi:10.1504/ijccbs.2018.096439

Cited by 20 publications

(9 citation statements)

References 22 publications

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“…Such models enable checking both safety and liveness properties and they have been particularly developed and studied over the last years. Model checkers such as UPPAAL [6], PRISM [22] and SBIP [23] have been proposed for analyzing timed and probabilistic properties of systems. Timed automata are used in [24] for studying fault tolerant behavior (safety, bounded liveness) in distributed asynchronous real time systems.…”

Section: Related Workmentioning

confidence: 99%

Timed protocol analysis of interconnected mobile IoT devices

Bouloukakis

Georgantas

Kattepur³

et al. 2021

J Internet Serv Appl

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With the emergence of the Internet of Things (IoT), application developers can rely on a variety of protocols and Application Programming Interfaces (APIs) to support data exchange between IoT devices. However, this may result in highly heterogeneous IoT interactions in terms of both functional and non-functional semantics. To map between heterogeneous functional semantics, middleware connectors can be utilized to interconnect IoT devices via bridging mechanisms. In this paper, we make use of the Data eXchange (DeX) connector model that enables interoperability among heterogeneous IoT devices. DeX interactions, including synchronous, asynchronous and streaming, rely on generic post and get primitives to represent IoT device behaviors with varying space/time coupling. Nevertheless, non-functional time semantics of IoT interactions such as data availability/validity, intermittent connectivity and application processing time, can severely affect response times and success rates of DeX interactions. We introduce timing parameters for time semantics to enhance the DeX API. The new DeX API enables the mapping of both functional and time semantics of DeX interactions. By precisely studying these timing parameters using timed automata models, we verify conditions for successful interactions with DeX connectors. Furthermore, we statistically analyze through simulations the effect of varying timing parameters to ensure higher probabilities of successful interactions. Simulation experiments are compared with experiments run on the DeX Mediators (DeXM) framework to evaluate the accuracy of the results. This work can provide application developers with precise design time information when setting these timing parameters in order to ensure accurate runtime behavior.

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Section: Related Workmentioning

confidence: 99%

Timed protocol analysis of interconnected mobile IoT devices

Bouloukakis

Georgantas

Kattepur³

et al. 2021

J Internet Serv Appl

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“…SBIP [83] is a statistical model checker for the BIP (Behavior, Interaction, Priority) general framework for the design of component-based systems that were developed at Verimag (see [84]).…”

Section: Sbipmentioning

confidence: 99%

Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review

2020

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The ever-increasing deployment of autonomous Cyber-Physical Systems (CPSs) (e.g., autonomous cars, UAV) exacerbates the need for efficient formal verification methods. In this setting, the main obstacle to overcome is the huge number of scenarios to be evaluated. Statistical Model Checking (SMC) is a simulation-based approach that holds the promise to overcome such an obstacle by using statistical methods in order to sample the set of scenarios. Many SMC tools exist, and they have been reviewed in several works. In this paper, we will overview Monte Carlo-based SMC tools in order to provide selection criteria based on Key Performance Indicators (KPIs) for the verification activity (e.g., minimize verification time or cost) as well as on the environment features, the kind of system model, the language used to define the requirements to be verified, the statistical inference approach used, and the algorithm implementing it. Furthermore, we will identify open research challenges in the field of (SMC) tools.

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“…In their work in [39] extended the Behavior Interaction Priority (BIP) [40] formalism to the stochastic formalism of Stochastic BIP (SBIP) [41] based on timed automata for enriched compositional modeling. They used Probabilistic BLTL (PBLTL)-based SMC algorithms to verify the properties of SBIP models, focusing particularly on performance evaluation.…”

Section: Related Workmentioning

confidence: 99%

SAM-SoS: A Stochastic Software Architecture Modeling and Verification Approach for Complex System-of-Systems

et al. 2020

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A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any potential impact on the properties critical for achieving the missions. Current modeling approaches lack the essential syntax and semantics required to model and verify SoS behaviors at design time and cannot offer alternative design choices for better design decisions. Therefore, the majority of existing techniques fail to provide qualitative and quantitative verification of SoS architecture models. Consequently, we have proposed an approach to model and verify Non-Deterministic (ND) SoS in advance by extending the current algebraic notations for the formal models as a hybrid stochastic formalism to specify and reason architectural elements with the required semantics. A formal stochastic model is developed using a hybrid approach for architectural descriptions of SoS with behavioral constraints. Through a model-driven approach, stochastic models are then translated into PRISM using formal verification rules. The effectiveness of the approach has been tested with an end-to-end case study design of an emergency response SoS for dealing with a fire situation. Architectural analysis is conducted on the stochastic model, using various qualitative and quantitative measures for SoS missions. Experimental results reveal critical aspects of SoS architecture model that facilitate better achievement of missions and QAs with improved design, using the proposed approach.

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Performance evaluation of stochastic real-time systems with the SBIP framework

Cited by 20 publications

References 22 publications

Timed protocol analysis of interconnected mobile IoT devices

Timed protocol analysis of interconnected mobile IoT devices

Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review

SAM-SoS: A Stochastic Software Architecture Modeling and Verification Approach for Complex System-of-Systems

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