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

Abstract

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.

Towards Collective Sentiment Analysis in IoT-Enabled Scenarios

“…• enhancing the context-awareness of the system but still preserving users' privacy; • providing a more detailed description of the domain, according, for example, to the high-level metamodels of [26], also for fostering the integration of the Smart Museum in Digital Libraries and other Web-based systems; • identifying, testing and embedding suitable SA models in the system; • estimating the deployment performance of smart collective services at the edge according to the methodology and simulation-based tool-chain presented in [27].…”

Model-Based System Engineering of the Internet of Things: A Bibliometric Literature Analysis

“…Based on Protelis and EdgeCloudSim, a model-based toolchain is susggested for deployment performance estimation and simulation-based evaluation [62]. To enhance IoT system operation capabilities and constraint definition, certain modeling and verification approaches are also integrated with MBSE.…”

The Exchange Calculus (Xc): A Functional Programming Language Design for Distributed Collective Systems

“…𝖷𝖢 can be framed in the context of a long-term research thread on programming languages and tools for programming collective adaptive systems, known under the umbrella terms of field-based coordination (Mamei and Zambonelli, 2006;Viroli et al, 2019) and aggregate computing (Beal et al, 2015;Viroli et al, 2019). This research area is characterised by works on formal calculi (Audrito et al, 2019(Audrito et al, , 2023a, new constructs (Audrito et al, 2020;Casadei et al, 2019), formal properties of programs and computations (Viroli et al, 2018;Beal et al, 2017;Audrito et al, 2018a), programming language implementations of formal calculi as DSLs (Casadei et al, 2022b(Casadei et al, , 2021Audrito, 2020), simulators (Pianini et al, 2013;Audrito et al, 2022e), algorithms and patterns (Beal, 2009;Audrito et al, 2017b,a;Pianini et al, 2021b;Audrito et al, 2021a;Pianini et al, 2022), execution models (Pianini et al, 2021a), distributed platforms and deployments (Casadei et al, 2020(Casadei et al, , 2022a, and libraries for application domains such as swarm robotics (Aguzzi et al, 2023) and distributed monitoring (Audrito et al, 2021b). In a nutshell, this work proposes a new calculus, 𝖷𝖢, inspired by previous calculi, that subsumes them and is strictly more expressive in its ability to model messages differentiated on a neighbour basis (see Section 8.3 for a more detailed comparison).…”