Architectural Issues of Adaptive Pervasive Systems

Caporuscio, Mauro; Funaro, Marco; Ghezzi, Carlo

doi:10.1007/978-3-642-17322-6_21

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Such systems demand techniques that let software react to changes by self-organizing its structure and self-adapting its behavior [19][20][21]. Many works [22] have shown the benefits of using component-based approaches in such open-world environments [23][24][25].…”

Section: Component-based Software Architecturementioning

confidence: 99%

ScapeGoat: Spotting abnormal resource usage in component-based reconfigurable software systems

Gonzalez-Herrera

Bourcier

Daubert

et al. 2016

Journal of Systems and Software

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Modern component frameworks support continuous deployment and simultaneous execution of multiple software components on top of the same virtual machine. However, isolation between the various components is limited. A faulty version of any one of the software components can compromise the whole system by consuming all available resources. In this paper, we address the problem of efficiently identifying faulty software components running simultaneously in a single virtual machine. Current solutions that perform permanent and extensive monitoring to detect anomalies induce high overhead on the system, and can, by themselves, make the system unstable. In this paper we present an optimistic adaptive monitoring system to determine the faulty components of an application. Suspected components are finely analyzed by the monitoring system, but only when required. Unsuspected components are left untouched and execute normally. Thus, we perform localized just-in-time monitoring that decreases the accumulated overhead of the monitoring system. We evaluate our approach on two case studies against a state-of-the-art monitoring system and show that our technique correctly detects faulty components, while reducing overhead by an average of 93%.

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Section: Component-based Software Architecturementioning

confidence: 99%

ScapeGoat: Spotting abnormal resource usage in component-based reconfigurable software systems

Gonzalez-Herrera

Bourcier

Daubert

et al. 2016

Journal of Systems and Software

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“…This may happen, for example, in the case of Web service-based systems [6,7]. This also happens in pervasive computing scenarios where devices that run application components are mobile [27]. Because of mobility, and more generally context change, certain components may become unreachable, while others become visible during the application's lifetime.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Verification at Runtime for Self-Adaptive Systems

Filieri

Tamburrelli

2013

Assurances for Self-Adaptive Systems

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Abstract. An effective design of effective and efficient self-adaptive systems may rely on several existing approaches. Software models and model checking techniques at run time represent one of them since they support automatic reasoning about such changes, detect harmful configurations, and potentially enable appropriate (self-)reactions. However, traditional model checking techniques and tools may not be applied as they are at run time, since they hardly meet the constraints imposed by on-the-fly analysis, in terms of execution time and memory occupation. For this reason, efficient run-time model checking represents a crucial research challenge. This paper precisely addresses this issue and focuses on probabilistic run-time model checking in which reliability models are given in terms of Discrete Time Markov Chains which are verified at run-time against a set of requirements expressed as logical formulae. In particular, the paper discusses the use of probabilistic model checking at run-time for selfadaptive systems by surveying and comparing the existing approaches divided in two categories: state-elimination algorithms and algebra-based algorithms. The discussion is supported by a realistic example and by empirical experiments.

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“…As for the architectural layer, applications should support adaptive and evolutionary situation-aware behaviors. Adaptation refers to the ability to react to environmental changes to keep satisfying the requirements, whereas evolution refers to the ability of satisfying new or different requirements [9]. In order to be selfadaptable and easily evolvable, applications should exploit design models able to: (i) deal with the run-time growth of the application in terms of involved resources (flexibility), (ii) accommodate heterogeneous and unforeseen functionalities into the running application (genericity), and (iii) discover new functionalities at run time and rearrange the application accordingly (dynamism).…”

Section: Introductionmentioning

confidence: 99%