Security-Aware Resource Allocation in Fog Computing Environments

Abstract Fog computing, an innovative approach building upon cloud technology, taps into local user resources to enhance services. It boasts cost-effectiveness, improved security, and reduced network delays, making it a popular choice for real-time applications. However, due to the heterogeneity of fog devices, resource allocation and scheduling pose challenges. This paper introduces a novel solution using a crow-inspired search mechanism and a multi-objective evolutionary approach for fog computing environments. The primary objectives are to optimize security and success rates simultaneously. A local search method enhances the performance of the crow search algorithm (CSA). The proposed method utilizes evolutionary techniques to allocate and schedule resources effectively. We compared our hybrid CSA with the original CSA and Genetic Algorithm (GA) across seven scenarios with varying parameters. Our hybrid CSA consistently outperformed existing methods, demonstrating its effectiveness in achieving the desired objectives.