2021
DOI: 10.1002/spe.2986
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Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

Abstract: Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end‐users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state‐of‐the‐art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed t… Show more

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Cited by 41 publications
(21 citation statements)
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“…Deng et al [38] have proposed different solutions for service placement, including a bat-inspired algorithm and a proximity-based approach utilizing Q-learning and ARIMA algorithms. Salimian et al [39] have examined the issue of deploying IoT applications on fog nodes using the concept of selfmanagement. Due to the heterogeneous resources and constraints of fog nodes, the GWO algorithm has been used to effectively deploy IoT applications on existing fog nodes and remove constraints.…”
Section: Centralized and Decentralized Approachesmentioning
confidence: 99%
“…Deng et al [38] have proposed different solutions for service placement, including a bat-inspired algorithm and a proximity-based approach utilizing Q-learning and ARIMA algorithms. Salimian et al [39] have examined the issue of deploying IoT applications on fog nodes using the concept of selfmanagement. Due to the heterogeneous resources and constraints of fog nodes, the GWO algorithm has been used to effectively deploy IoT applications on existing fog nodes and remove constraints.…”
Section: Centralized and Decentralized Approachesmentioning
confidence: 99%
“…Studies reveal that the existing RA works focused more on the development of frameworks that reduced latency and increased the quality of service [10]. The works carried out using feed-forward NN, MINLP, and other optimization techniques also obtained better outcomes [11]. But these techniques were heuristic-based and time-consuming.…”
Section: Monte Carlo (Mc) Mc-exploring Starts (Mc-es) Andmentioning
confidence: 99%
“…Fog's limited resource forms the constraint of the resource allocation problem. The objective of the FogRA system is expressed as a multi-objective optimization problem as given in the equation (11).…”
Section: A System Modelmentioning
confidence: 99%
“…Salimian M et al [6]proposed an autonomous IoT service placement method based on gray wolf optimization scheme, which improves system performance while considering execution costs. In order to minimize the delay between the switch and the controller, Li B et al [7] proposed a controller placement strategy based on Louvain's algorithm.…”
Section: Reliable Controller Placementmentioning
confidence: 99%