The Internet of Things (IoT) encompasses both large-scale deployed physical infrastructures and software layers that enable intuitive and transparent creation of applications. This highly distributed, energy-greedy environment must ensure the quality of deployed services while taking into account the heterogeneity of capabilities and protocols as well as users and objects mobility. Deployment infrastructure has been redesigned to provide the necessary features, including paradigms such as software-defined networks and Fog computing. The purpose of this article is to study IoT services placement in a Fog architecture. We propose a model of the infrastructure and IoT applications as well as a placement strategy taking into account system's energy consumption and applications delay violations minimization with a Discrete Particles Swarm Optimization algorithm (DPSO). Simulations have been done with iFogSim simulator. Results have been compared with heuristics coming from the literature: Binary Partical Swarm optimization (BPSO), Dicothomous Module Mapping (DCT), CloudOnly, IoTFogOnly, IoTCloud (IC) and FogCloud (FC) placement approaches.
Fog computing has emerged as a strong distributed computation paradigm to support applications with stringent latency requirements. It offers almost ubiquitous computation capacities over a large geographical area. However, Fog systems are highly heterogeneous and dynamic which makes services placement decision quite challenging considering nodes mobility that may decrease the placement decision quality over time. This paper proposes a Mobility-aware Genetic Algorithm (MGA) for services placement in the Fog which aims at supporting nodes' mobility while ensuring both infrastructures energy-efficiency and applications Quality of Service (QoS) requirements. We have compared this approach with two variants of Shortest Access Point migration strategy (SAP) from the literature, a proposed Mobility Greedy Heuristic (MGH) and a baseline Simple Genetic Algorithm (SGA). Experiments conducted with MyiFogSim simulator have shown that MGA ensures good performances in terms of energy and delay violations minimization compared to other methods.
Mobility of Internet of Things (IoT) objects is a key characteristic of IoT environments. It brings dynamicity, uncertainty and raises many challenges when it is associated with computation and network resources management for IoT applications. The resources management problem under objects mobility consideration is even more sensitive if we consider that various IoT applications have stringent Quality of Service (QoS) needs. Fog Computing is a distributed computation paradigm that increases data centers computation and storage abilities with nodes between end-users and the Cloud. Fog computing offers a large distributed infrastructure to support IoT applications needs by bringing services closer to end users. However, Fog infrastructures inherit the energy greediness characteristics of both data centers and network infrastructures. This work investigates the IoT services placement problem in the Fog as an optimization problem to minimize energy consumption and enhance QoS while considering mobility of IoT objects. We model the placement problem as a multi-objective optimization problem and we propose a location history based mobility model (HTM) to estimate future locations of IoT mobile nodes. We propose a framework composed of online strategies for IoT services placement and a Mobility-aware Genetic Algorithm (MGA) for services migrations. We evaluate our strategies through iFogSim simulator and compare the proposed framework to migrations and placement strategies from the literature based on Shortest Access Point migration strategy (SAP) and with Penguins Search Optimization Algorithm (PeSOA). Experiments show that the proposed framework outperforms literature approaches for the considered objectives and for various configurations of the mobile environment.
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