Statistical Model Checking QoS Properties of Systems with SBIP

Bensalem, Saddek; Bozga, Marius; Delahaye, Benoît; Jégourel, Cyrille; Legay, Axel; Nouri, Ayoub

doi:10.1007/978-3-642-34026-0_25

Cited by 20 publications

(24 citation statements)

References 28 publications

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“…The construction of the SBIP System Model also allowed the analysis and validation of the aforementioned non-functional requirements for the case study. Therefore, we accordingly describe them with stochastic temporal properties using the Probabilistic Bounded Linear Temporal Logic (PBLTL) formalism [10] and present their evaluation results through the SBIP model checking tool. NFR1.…”

Section: Methodsmentioning

confidence: 99%

“…Consider a system model M and a set of non-functional requirements R 1 ....R n . Each requirement is formalized by a stochastic temporal property φ written in the Probabilistic Bounded Linear Temporal Logic (PBLTL) [10]. SMC is then used to apply a series of simulation-based analyses in order to answer two questions: (1) qualitative: is the probability P r M (φ) for M to satisfy φ greater or equal to a threshold θ?…”

Section: B Statistical Model Checking (Smc)mentioning

confidence: 99%

“…SBIP [10] is a BIP extension that relies on a stochastic semantics, for the verification of large-scale systems by using Statistical Model Checking ( [11] [12] properties connected to non-functional requirements. Figure 2 shows two atomic components from our IoT system model in BIP.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using BIP to reinforce correctness of resource-constrained IoT applications

Lekidis

Stachtiari

Katsaros

et al. 2015

10th IEEE International Symposium on Industrial Embedded Systems (SIES)

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Abstract-Internet of Things (IoT) systems process and respond to multiple (external) events, while performing computations for a Sense-Compute-Control (SCC) or a Sense-Only (SO) goal. Given the limitations of the interconnected resource-constrained devices, the execution environment can be based on an appropriate operating system for the IoT. The development effort can be reduced, when applications are built on top of RESTful web services, which can be shared and reused. However, the asynchronous communication between remote nodes is prone to event scheduling delays, which cannot be predicted and taken into account while programming the application. Long delays in message processing and communication, due to packet collisions, are avoided by carefully choosing the data transmission frequencies between the system's nodes. But even when specialized simulators are available, it is still a hard challenge to guarantee the functional and non-functional requirements at the application and system levels. In this article, we introduce a model-based rigorous analysis approach using the BIP component framework. We present a BIP model for IoT applications running on the Contiki OS. At the application level, we verify qualitative properties for service responsiveness requirements, whereas at the system level we can validate qualitative and quantitative properties using statistical model checking. We present results for an application scenario running on a distributed system infrastructure. I . INTRODUCTIONThe main challenge in the design of systems for the Internet of Things (IoT) is to implement a lightweight architecture with abstractions for an appropriate execution environment, while staying within the resource limitations of the interconnected devices. Such an environment can be based on existing IoT operating systems ([1], [2], [3]), which facilitate system integration by abstracting hardware and allowing control of the system's nodes.In this context, applications are implemented as event-driven systems with processes acting as event handlers that run to completion. Due to the resource limitations and under the condition that an event handler cannot block, all processes of a node share the same stack. When an event is destined for a process, the process is scheduled and the event -along with accompanying data -is delivered to the process through the activation of its event handler.IoT operating systems decouple the applications' design from the low-level kernel functions, which provide CPU multiplexing and event scheduling. Thus, the development of IoT applications can proceed independently from their deployment, which has the advantages of programming at a higher-level, but opens a possibility for design errors at the overall system level. Depending on the way that IoT applications are eventually deployed in a distributed environment, they may have to handle and route many different types of events [4]. In general, it is hard to ensure seamless interactions between the system's components given their high heterogeneit...

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Section: Methodsmentioning

confidence: 99%

Section: B Statistical Model Checking (Smc)mentioning

confidence: 99%

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Using BIP to reinforce correctness of resource-constrained IoT applications

Lekidis

Stachtiari

Katsaros

et al. 2015

10th IEEE International Symposium on Industrial Embedded Systems (SIES)

Self Cite

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“…Although there are many different software engineering methods and tools to handle awareness and adaptation capabilities of autonomic systems following the ensemble approach at design time (i.e. requirements engineering [1,2]; modeling and programming [13,12]; and verification and validation [4,3]), very little work exists to analyze and observe these capabilities at runtime. This provides motivation for the current research.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and visualizing adaptation of autonomic systems at runtime

Abeywickrama

Serbedzija

Loreti

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Monitoring an autonomic system at runtime, which typically contains a large number of nodes operating in highly dynamic and open-ended environments, is very challenging for software architects. Solid software engineering methods and tools to support this process are therefore highly required. This paper proposes a novel monitoring and visualization framework for autonomic systems at runtime. Our approach is illustrated by an Eclipse plug-in for tracing the runtime awareness and adaptation capabilities using graph-like representation. A key benefit here is to provide feedback to the engineer about the behavior of the complex awareness mechanism used, thus helping the system evaluation process. We validate and assess our approach and plug-in with a concrete application scenario from swarm robotics domain

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“…SMC combines Monte-Carlo simulation [11] on model traces with statistical techniques in order to decide whether some stochastic model satisfies a given property or to compute its satisfaction probability. Nowadays, SMC is getting increased industrial attention [4] and several modeling and/or analysis frameworks include it amongst their (usually, most successful) analysis techniques [5,16,15,3].…”

Section: Introductionmentioning

confidence: 99%

Faster Statistical Model Checking by Means of Abstraction and Learning

Nouri

Raman

Bozga

et al. 2014

Runtime Verification

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International audienceThis paper investigates the combined use of abstraction and probabilistic learning as a means to enhance statistical model checking performance. We are given a property (or a list of properties) for verification on a (large) stochastic system. We project on a set of traces generated from the original system, and learn a (small) abstract model from the projected traces, which contain only those labels that are relevant to the property to be verified. Then, we model-check the property on the reduced, abstract model instead of the large, original system. In this paper, we propose a formal definition of the projection on traces given a property to verify. We also provide conditions ensuring the correct preservation of the property on the abstract model. We validate our approach on the Herman's Self Stabilizing protocol. Our experimental results show that (a) the size of the abstract model and the verification time are drastically reduced, and that (b) the probability of satisfaction of the property being verified is correctly estimated by statistical model checking on the abstract model with respect to the concrete system

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Statistical Model Checking QoS Properties of Systems with SBIP

Cited by 20 publications

References 28 publications

Using BIP to reinforce correctness of resource-constrained IoT applications

Using BIP to reinforce correctness of resource-constrained IoT applications

Monitoring and visualizing adaptation of autonomic systems at runtime

Faster Statistical Model Checking by Means of Abstraction and Learning

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