Abstract
Fog computing is an emerging computing paradigm that extends traditional cloud computing by leveraging the resources at the user premises for providing better services. It is preferred for many real-time applications because of its advantages such as reduced network latency, improved security, and reduced operational costs. Due to the inherent heterogeneity among the fog devices, resource allocation and scheduling is a challenging task. This paper utilizes a multi-objective population-based metaheuristic optimizer called the crow search algorithm for resource allocation and scheduling in the fog computing environment. The two different objectives considered by the proposed work are namely: success ratio and the security hit ratio. Both of these objectives need to be maximized. To enhance the performance of the crow search algorithm, a local search method is utilized. The proposed work applies the metaheuristic technique for solving resource allocation and scheduling in the fog environment. The performance of the proposed algorithm is compared with the other existing algorithms, and the comparison results demonstrate the efficiency of the proposed algorithms in achieving the stated objectives.
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Subbaraj, S., Thiyagarajan, R. & Rengaraj, M. A smart fog computing based real-time secure resource allocation and scheduling strategy using multi-objective crow search algorithm. J Ambient Intell Human Comput 14, 1003–1015 (2023). https://doi.org/10.1007/s12652-021-03354-y
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DOI: https://doi.org/10.1007/s12652-021-03354-y