Ten Years of Self-adaptive Systems: From Dynamic Ensembles to Collective Adaptive Systems

Bucchiarone, Antonio; Mongiello, Marina

doi:10.1007/978-3-030-30985-5_3

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…In this section, we review the state of the art on CAS engineering, which aims to analyze and design the emergent behavior of large-scale situated cyber-physical systems. Example applications include crowd engineering for safe navigation and dispersal [15], smart mobility [16], situated problem solving [9], trust and reputation systems [17], robotics [18], [19], and resilient management of ICT infrastructures [20]. A recent survey of models, methods, and tools for rigorous CAS engineering can be found in [21].…”

Section: A Collective Adaptive Systems Developmentmentioning

confidence: 99%

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Casadei¹,

Fortino

Pianini³

et al. 2022

IEEE Internet Things J.

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Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)-edge-fog-cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for "pulverization": applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT-edge-fog-cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings.

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Section: A Collective Adaptive Systems Developmentmentioning

confidence: 99%

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Casadei¹,

Fortino

Pianini³

et al. 2022

IEEE Internet Things J.

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“…Most of these works show a trend in self-organization, shifting from natural inspiration to the identification of "artificial" mechanisms for engineering the collective part of adaptive systems, namely to develop so-called collective adaptive systems (CASs). Research on CASs [34] study how a collective (i.e., a dynamic group of "similar" autonomous entities-e.g., a swarm of robots) can support macro-level adaptivity and how such insights can be leveraged for engineering large-scale socio-technical systems.…”

Section: Self-organizationmentioning

confidence: 99%

Pulverization in Cyber-Physical Systems: Engineering the Self-Organizing Logic Separated from Deployment

et al. 2020

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Emerging cyber-physical systems, such as robot swarms, crowds of augmented people, and smart cities, require well-crafted self-organizing behavior to properly deal with dynamic environments and pervasive disturbances. However, the infrastructures providing networking and computing services to support these systems are becoming increasingly complex, layered and heterogeneous—consider the case of the edge–fog–cloud interplay. This typically hinders the application of self-organizing mechanisms and patterns, which are often designed to work on flat networks. To promote reuse of behavior and flexibility in infrastructure exploitation, we argue that self-organizing logic should be largely independent of the specific application deployment. We show that this separation of concerns can be achieved through a proposed “pulverization approach”: the global system behavior of application services gets broken into smaller computational pieces that are continuously executed across the available hosts. This model can then be instantiated in the aggregate computing framework, whereby self-organizing behavior is specified compositionally. We showcase how the proposed approach enables expressing the application logic of a self-organizing cyber-physical system in a deployment-independent fashion, and simulate its deployment on multiple heterogeneous infrastructures that include cloud, edge, and LoRaWAN network elements.

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“…CPCs can be approached from several theoretical and engineering perspectives. Informative, but non-exhaustive, exemplars include: • cyber-physical and hybrid systems ( Cassandras and Lafortune, 2008 ; Kim and Kumar 2012 ); • coordination models and languages ( Malone and Crowston, 1994 ; Ciatto et al, 2020 ); • autonomic computing and self-* systems ( Kephart and Chess, 2003 ; de Lemos et al, 2010 ; Bellman et al, 2021 ; Harvey et al, 2021 ); • artificial life ( Aguilar et al, 2014 ; Gershenson, 2023 ); • multi-agent systems (MASs) ( Wooldridge, 2009 ; Mascardi et al, 2019 ; Boissier et al, 2020 ); • grammar systems ( Klavins et al, 2006 ; Csuhaj-Varjú et al, 2018 ); • swarm robotics ( Brambilla et al, 2013 ; Dorigo et al, 2021 ); • collective intelligence ( Malone and Bernstein, 2022 ; Casadei, 2023a ); • complex adaptive systems ( Bucchiarone and Mongiello, 2019 ; Abeywickrama et al, 2020 ; Wirsing et al, 2023 ); • multi-agent reinforcement learning ( Zhang et al, 2019 ); and • macro-programming ( Júnior et al, 2022 ; Casadei, 2023b ). …”

Section: Introductionmentioning

confidence: 99%

“…• complex adaptive systems ( Bucchiarone and Mongiello, 2019 ; Abeywickrama et al, 2020 ; Wirsing et al, 2023 );…”

Section: Introductionmentioning

confidence: 99%

Editorial: Understanding and engineering cyber-physical collectives

Casadei,

Esterle,

Gamble

et al. 2024

Front. Robot. AI

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Ten Years of Self-adaptive Systems: From Dynamic Ensembles to Collective Adaptive Systems

Cited by 8 publications

References 28 publications

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Pulverization in Cyber-Physical Systems: Engineering the Self-Organizing Logic Separated from Deployment

Editorial: Understanding and engineering cyber-physical collectives

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