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

Research trends are pushing artificial intelligence (AI) across the IoT-Edge-Fog-Cloud continuum, to enable effective data analytics, decision making, as well as efficient use of resources for QoS targets. Approaches for collective adaptive systems engineering, such as aggregate computing, provide d...

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Veröffentlicht in:	IEEE internet of things journal Jg. 9; H. 20; S. 1
Hauptverfasser:	Casadei, Roberto, Fortino, Giancarlo, Pianini, Danilo, Placuzzi, Andrea, Savaglio, Claudio, Viroli, Mirko
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 15.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive systems Aggregates Artificial intelligence Cloud Services Collective Services Computational modeling Computer architecture Costs Cyber-Physical Systems Decision analysis Decision making Declarative programming Deployment methodology Edge Intelligence Heterogeneity Infrastructure Internet of Things Micromodules Mobile and Ubiquitous Systems Multimedia Programming Pulverisable Architectures Service Middleware and Platform Simulation
ISSN:	2327-4662, 2327-4662
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Abstract	Research trends are pushing artificial intelligence (AI) across the IoT-Edge-Fog-Cloud continuum, to enable effective data analytics, decision making, as well as efficient use of resources for QoS targets. Approaches for collective adaptive systems 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 "pulverisation": applications can be decomposed into many deployable micro-modules 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-organising into clusters to promote load-balancing in large-scale dynamic settings.
AbstractList	Research trends are pushing artificial intelligence (AI) across the IoT-Edge-Fog-Cloud continuum, to enable effective data analytics, decision making, as well as efficient use of resources for QoS targets. Approaches for collective adaptive systems 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 "pulverisation": applications can be decomposed into many deployable micro-modules 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-organising into clusters to promote load-balancing in large-scale dynamic settings. 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.
Author	Placuzzi, Andrea Casadei, Roberto Fortino, Giancarlo Savaglio, Claudio Pianini, Danilo Viroli, Mirko
Author_xml	– sequence: 1 givenname: Roberto surname: Casadei fullname: Casadei, Roberto organization: Department of Computer Science and Engineering (DISI), Alma Mater Studiorum-Università di Bologna, 47522 Cesena, FC, Italy – sequence: 2 givenname: Giancarlo surname: Fortino fullname: Fortino, Giancarlo organization: Department of Informatics, Modeling, Electronics and Systems (DIMES), Università della Calabria, 87036 Arcavacata, Rende CS, Italy – sequence: 3 givenname: Danilo surname: Pianini fullname: Pianini, Danilo organization: Department of Computer Science and Engineering (DISI), Alma Mater Studiorum-Università di Bologna, 47522 Cesena, FC, Italy – sequence: 4 givenname: Andrea surname: Placuzzi fullname: Placuzzi, Andrea organization: Department of Computer Science and Engineering (DISI), Alma Mater Studiorum-Università di Bologna, 47522 Cesena, FC, Italy – sequence: 5 givenname: Claudio surname: Savaglio fullname: Savaglio, Claudio organization: Institute for High Performance Computing and Networking (ICAR), National Research Council, 87036 Arcavacata, Rende CS, Italy, Italy – sequence: 6 givenname: Mirko surname: Viroli fullname: Viroli, Mirko organization: Department of Computer Science and Engineering (DISI), Alma Mater Studiorum-Università di Bologna, 47522 Cesena, FC, Italy
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SubjectTerms	Adaptive systems Aggregates Artificial intelligence Cloud Services Collective Services Computational modeling Computer architecture Costs Cyber-Physical Systems Decision analysis Decision making Declarative programming Deployment methodology Edge Intelligence Heterogeneity Infrastructure Internet of Things Micromodules Mobile and Ubiquitous Systems Multimedia Programming Pulverisable Architectures Service Middleware and Platform Simulation
Title	A Methodology and Simulation-based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge
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