A novel hybrid task scheduling method in fog computing using reinforcement learning and metaheuristic algorithms

Abstract The widespread use of Internet of Things (IoT) devices has brought along the importance of efficient resource management in fog computing systems in order to optimize the quality of experience of users. Task scheduling in fog computing systems acts as a bridge between tasks and the available resources, necessitating the use of advanced scheduling algorithms to optimize the parameters, such as deadline, response time, energy, load balancing, and cost. This paper describes the Novel Task Scheduling Approach for fog computing, named the Novel Task Scheduling Method (NTSM). In NTSM, the fog computing nodes are predicted, and the predicted values are partitioned into heavy and light groups for efficient allocation of tasks. This scalable approach optimizes the irregular cellular learning automata using the concept of the artificial rabbit algorithm for the allocation of tasks in the heavy groups, while the meta-heuristic algorithm optimizes the tasks in the light groups. The proposed approach has been proved effective in simulating the resource management process in fog computing, pertaining to the use of the IoT, in terms of optimized usage of energy, load balancing, deadline satisfaction, minimized response times, and reduced costs.