The pervasiveness and the growing processing capabilities of mobile and embedded computing systems are leading to a shift from the Internet of Things (IoT) paradigm to the Fog computing scenario where the environment is instrumented with high-performance computing in the proximity to cyber–physical systems. The design of such systems requires an accurate planning, on the one hand, to ensure that specific application requirements will be properly met at run-time, and, on the other hand, to minimize the system's monetary costs. In this paper we present a methodology for an automated design and deployment of distributed cyber–physical systems into smart environments. We propose an engine based on a Mixed Integer Linear Programming (MILP) formulation which takes in input a planimetry of the environment and a description of the applications and, based on a repository of available processing boards, identifies the cost-optimized instantiation of the processing architecture and the corresponding distribution of the application functionalities. By comparing our proposal with the existing methodologies that address similar problems we can highlight the following novelties: (i) we address a system architecture composed of heterogeneous devices, (ii) we adopt a realistic model of the environment, and (iii) we perform a joint co-exploration of architecture instantiation and applications mapping. An experimental evaluation, considering a smart office case study, demonstrates the potential of the proposed approach in minimizing the overall system monetary cost around 42% w.r.t. a baseline approach not exploiting planimetry information. Such results have been also confirmed by an extensive experimental campaign using synthetic problems, which also highlighted how the execution times of the optimization process are affordable for the design-time process.

A methodology for the design and deployment of distributed cyber–physical systems for smart environments

Cassano L.;Miele A.;
2020

Abstract

The pervasiveness and the growing processing capabilities of mobile and embedded computing systems are leading to a shift from the Internet of Things (IoT) paradigm to the Fog computing scenario where the environment is instrumented with high-performance computing in the proximity to cyber–physical systems. The design of such systems requires an accurate planning, on the one hand, to ensure that specific application requirements will be properly met at run-time, and, on the other hand, to minimize the system's monetary costs. In this paper we present a methodology for an automated design and deployment of distributed cyber–physical systems into smart environments. We propose an engine based on a Mixed Integer Linear Programming (MILP) formulation which takes in input a planimetry of the environment and a description of the applications and, based on a repository of available processing boards, identifies the cost-optimized instantiation of the processing architecture and the corresponding distribution of the application functionalities. By comparing our proposal with the existing methodologies that address similar problems we can highlight the following novelties: (i) we address a system architecture composed of heterogeneous devices, (ii) we adopt a realistic model of the environment, and (iii) we perform a joint co-exploration of architecture instantiation and applications mapping. An experimental evaluation, considering a smart office case study, demonstrates the potential of the proposed approach in minimizing the overall system monetary cost around 42% w.r.t. a baseline approach not exploiting planimetry information. Such results have been also confirmed by an extensive experimental campaign using synthetic problems, which also highlighted how the execution times of the optimization process are affordable for the design-time process.
Distributed cyber–physical systems
Mixed integer linear programming
Smart environments
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1147086
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