Enhancing IoT service deployment in fog computing using metaheuristic methods: A novel approach

The present study investigates the application of metaheuristic schemes as a novel method to improve the implementation of IoT services in fog computing. Fog computing, an extension of cloud services at the network edge, provides a promising opportunity to serve time-sensitive applications by enabling computations on the collection of IoT devices. Leveraging the principles of the microservice architecture, this study focusses on the NP-hard task of allocating IoT services to fog nodes. The objectives of SPP-DEA include cost, time to serve, resources consumed, number of services supplied, resource consumption distribution, service deployment priority during placement, and effective control of computational complexity. Empirical assessment in synthetic fog validates the feasibility of SPP-DEA, demonstrating significant enhancements in service cost and response time when compared to conventional models. Analysis of algorithms in an experimental fog scenario demonstrates that SPP-DEA achieves a 16% reduction in service cost and a 10% reduction in waiting time when compared to current approaches.