Self-adaptive software needs quantitative verification at runtime

Călinescu, Radu; Ghezzi, Carlo; Kwiatkowska, Marta; Mirandola, Raffaela

doi:10.1145/2330667.2330686

Cited by 234 publications

(163 citation statements)

References 31 publications

Supporting

Mentioning

158

Contrasting

Unclassified

Order By: Relevance

“…Environment domain models are a key element used by adaptive systems to determine their behavior [1,18]. These models capture the knowledge that the system has about itself and its environment by describing how system and environment respond to adaptation actions.…”

Section: Related Workmentioning

confidence: 99%

“…The second category consists of approaches that consider the behavior of the system and its environment modeled in a monolithic way in terms of more powerful models defined at a lower level of abstraction [2,1,9]. For example, in the approach presented in [2], DTMCs are used to model, for each system configuration, the future state of a system and its environment if that configuration is used.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Impact Models for Architecture-Based Self-adaptive Systems

Moreno

Lopes

Garlan

et al. 2015

Formal Aspects of Component Software

View full text Add to dashboard Cite

Abstract. Self-adaptive systems have the ability to adapt their behavior to dynamic operation conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decisionmaking in the Rainbow framework for architecture-based self-adaptation.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Impact Models for Architecture-Based Self-adaptive Systems

Moreno

Lopes

Garlan

et al. 2015

Formal Aspects of Component Software

View full text Add to dashboard Cite

show abstract

“…Ubiquitous computing systems such as home networks are enabled by service-based systems, typically based on cloud computing [17], which dynamically adapt behaviours to the changing requirements and contexts. It has been argued [6] that the need to continuously provide reliability, dependability and performance guarantees for adaptive systems calls for quantitative runtime verification. This is different from offline quantitative verification performed at the design stage, as described in Section 2, where a model is developed and analysed pre-deployment in order to improve the design.…”

Section: Adaptivenessmentioning

confidence: 99%

Advances in Quantitative Verification for Ubiquitous Computing

Kwiatkowska

2013

Theoretical Aspects of Computing – ICTAC 2013

Self Cite

View full text Add to dashboard Cite

Abstract. Ubiquitous computing, where computers 'disappear' and instead sensor-enabled and software-controlled devices assist us in everyday tasks, has become an established trend. To ensure the safety and reliability of software embedded in these devices, rigorous model-based design methodologies are called for. Quantitative verification, a powerful technique for analysing system models against quantitative properties such as "the probability of a data packet being delivered within 1ms to a nearby Bluetooth device is at least 0.98", has proved useful by detecting and correcting flaws in a number of ubiquitous computing applications. In this paper, we focus on three key aspects of ubiquitous computing: autonomous behaviour, constrained resources and adaptiveness. We summarise recent advances of quantitative verification in relation to these aspects, illustrating each with a case study analysed using the probabilistic model checker PRISM. The paper concludes with an outline of future challenges that remain in this area.

show abstract

“…The main culprit is state-space explosion in conjunction with the inherent complexity of the analysis methods that are involved. A quantitative runtime verification approach was recently proposed [2,16,1] as an alternative, complementary analysis method. We adopt this approach, and focus on the following system characteristics:…”

Section: Introductionmentioning

confidence: 99%

“…In [26] a model checker executed from the current local state has been used to predict and prevent future inconsistencies in a distributed system. In the quantitative runtime setting, a number of approaches have been proposed for different types of models, to mention the autonomic approach of [2,1] for discrete-and continuous-time Markov chains, parametric techniques of [15,16] for discrete time Markov chains and the incremental approach of [21] for Markov decision processes. Partially observable Markov decision processes are known to be infeasible, but a promising partial approach to adversary generation was recently proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

Towards Communication-Based Steering of Complex Distributed Systems

Dräger

Kwiatkowska

2012

Large-Scale Complex IT Systems. Development, Operation and Management

Self Cite

View full text Add to dashboard Cite

Abstract. Quantitative verification is an established automated technique that can ensure predictability and dependability of software systems which exhibit probabilistic behaviour. Since offline usage of quantitative verification is infeasible for large-scale complex systems that continuously adapt to the changing environment, quantitative runtime verification was proposed as an alternative. Using an illustrative case study of communicating, distributed probabilistic processes, we formulate the problem of quantitative steering, a runtime technique that involves system monitoring, prediction of future errors, and enforcement of system's behaviour away from the error states. We consider a communicationbased variant of steering where enforcement is achieved by modifying the contents of communication channels. Our approach is based on stochastic games, where one player is the system and the other players assume the role of the controller, and hence steering reduces to finding a controller strategy that meets the given quantitative goal. We discuss the solution to the quantitative steering problem and its extensions inspired by complex real-world scenarios.

show abstract

Self-adaptive software needs quantitative verification at runtime

Abstract: Continually verify self-adaptation decisions taken by critical software in response to changes in the operating environment.

Cited by 234 publications

References 31 publications

Impact Models for Architecture-Based Self-adaptive Systems

Impact Models for Architecture-Based Self-adaptive Systems

Advances in Quantitative Verification for Ubiquitous Computing

Towards Communication-Based Steering of Complex Distributed Systems

Contact Info

Product

Resources

About