Multiprocessor task scheduling using multi-objective hybrid genetic Algorithm in Fog–cloud computing

Abstract

Multiprocessor task scheduling is an operation of processing more than two tasks simultaneously in the system. The Fog–cloud multiprocessor computing structures are the categories of exchanged collateral structures with great demand from its initiation. Like other networking systems, the existing fog–cloud system based on multiprocessor systems faces some challenges. Due to the availability of excess clients and various services, scheduling and energy consumption issues are challenging. The existing problems must be resolved with proper planning to reduce makespan and energy consumption. To obtain this, an optimal scheduling approach is required. The proposed approach presents a novel methodology called Hybrid Genetic Algorithm and Energy Conscious Scheduling for better scheduling tasks over the processors. Here Genetic Algorithm and Energy conscious scheduling model are integrated. When only a Genetic Algorithm is chosen for the task scheduling approach, it becomes computationally expensive. Energy consumption becomes a huge challenge as it does not cope with complexity, making it extremely difficult to schedule appropriate tasks. When choosing the proposed hybrid Genetic algorithm, these issues can be overcome by considering optimal solutions with minimized makespan and consumed energy. A Genetic Algorithm is used to generate three primary chromosomes using priority approaches. The allocated resources are optimized through the Energy Conscious Scheduling model, and the proposed method is implemented using MATLAB. The existing methods, including genetic algorithm, particle swarm optimization, gravitational search algorithm, ant colony optimization and round robin models, are compared with the proposed method, proven comparatively better than existing models.