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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. 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