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

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.

BarMan: A run-time management framework in the resource continuum

“…Differently, Mukherjee et al in [18] developed an algorithm to distribute the workload while reducing the overall response time and cost of computation. Tanganelli et al [19] developed a strategy to design and deploy the devices in a cyber-physical environment minimizing installation cost, it also considers the mapping of different tasks based on available resources and location. Similarly, authors in [20] consider the device's deployment problem, including also energy-related aspects.…”

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

“…In the application placement literature, various challenges limit the user‐centric metrics such as QoS and QoE. To address these challenges, reviewed papers attempt to utilize their proposed approaches that are mainly categorized in six classes: exact solutions 21‐26 that most of them are considered as a form of mixed integer programming (MIP) problem, framework‐based, 27‐29 heuristic‐based, 30‐33 machine learning‐based, 34‐37 metaheuristic‐based, 38‐40 and model‐based 41‐43 approaches. These approaches will be reviewed in the following.…”

“…Tanganelli et al 21 proposed an autonomous distributed system in which applications are deployed into fog nodes to achieve applications' requirements. Focusing on system optimization and cost minimization, they formulated their problem as a mixed integer linear programming (MILP).…”

“…Focusing on system optimization and cost minimization, they formulated their problem as a mixed integer linear programming (MILP). Similar to Reference 21, Kherraf et al 22 formulated their IoT‐enabled resource provisioning and delay‐aware application deployment problem as a MIP on multi‐access edge computing servers. Dissimilarly, Liang et al 23 utilized a greedy algorithm for their proposed service placement model.…”

“…Dissimilarly, Liang et al 23 utilized a greedy algorithm for their proposed service placement model. Like Reference 21, Zeng et al 24 formulated a joint service deployment and load balancing problem as a MILP to solve it, and they proposed a heuristic algorithm. On the other hand, Benamer et al 25 presented a heuristic‐based application placement strategy on fog nodes minimizing the overall applications' execution time.…”