On Model-Based Performance Analysis of Collective Adaptive Systems

Murgia, Maurizio; Pinciroli, Riccardo; Trubiani, Catia; Tuosto, Emilio

doi:10.1007/978-3-031-19759-8_17

Cited by 2 publications

(4 citation statements)

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“…Final comments, related and future work are in Sect. 6. This paper is a revised version of [28]. The main difference with respect to [28] is that here we extend our analysis with a new scenario, dubbed centralised.…”

Section: Introductionmentioning

confidence: 99%

“…6. This paper is a revised version of [28]. The main difference with respect to [28] is that here we extend our analysis with a new scenario, dubbed centralised. Also, we significantly revised Sect.…”

Section: Introductionmentioning

confidence: 99%

“…3.2). A new format of the messages is introduced to simplify the definition of the predicate ρ d : the position of the sender is now part of the sent message, while in [28] it was obtained through function pos. The presentation of Sect.…”

Section: Introductionmentioning

confidence: 99%

“…Sections 5 and 6 have also been revised. We extended the former to take into account the analysis of the centralised architecture and the latter to compare our work with other approaches that we could not include in [28].…”

Section: Introductionmentioning

confidence: 99%

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Comparing perfomance abstractions for collective adaptive systems

Murgia,

Pinciroli,

Trubiani

et al. 2023

Int J Softw Tools Technol Transfer

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Non-functional properties of collective adaptive systems (CAS) are of paramount relevance practically in any application. This paper compares two recently proposed approaches to quantitative modelling that exploit different system abstractions: the first is based on generalised stochastic Petri nets, and the second is based on queueing networks. Through a case study involving autonomous robots, we analyse and discuss the relative merits of the approaches. This is done by considering three scenarios which differ on the architecture used to coordinate the distributed components. Our experimental results assess a high accuracy when comparing model-based performance analysis results derived from two different quantitative abstractions for CAS.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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