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In this work, a Particle Swarm Optimization (PSO)-based method for load balancing in cloud-fog systems is presented, which tackles the dynamic and distributed settings' resource management issues. Effective load balancing techniques are required due to the complex interactions between fog and cloud computing in order to maximize resource usage and improve system performance. In order to reduce processing delays and boost overall system performance, the suggested PSO-based load balancing architecture makes use of the swarm intelligence concept to dynamically distribute jobs among fog nodes and the cloud. The efficiency of the PSO algorithm in reaching load equilibrium is shown by comprehensive simulations and performance assessments, highlighting its flexibility to changing workloads. Comparing the PSO-based load balancing strategy to conventional methods, the results show a considerable improvement in response times and resource utilization. Moreover, the PSO algorithm's distributed structure and scalability make it ideal for cloud-fog systems, where centralized management might not be feasible. This study adds to the current conversation on maximizing the benefits of fog and cloud computing in concert and provides a workable answer to load balancing problems in dynamic, heterogeneous environments.
Nayak et al. (Fri,) studied this question.