Pareto-optimized Hybrid Metaheuristic Scheduling for Energy-Efficient and Reliable Fog Computing

Excessive advancement in technology and the Internet of Things (IoT) have brought a revolution in Cloud Computing. However, a massive amount of data is generated from IoT devices, ultimately affecting the Cloud’s efficiency. Fog Computing has been developed to improve efficiency. It places Fog nodes near the IOT devices to reduce the processing latency. Fog computing has achieved remarkable success; however, its nodes are resource-constrained, which makes efficient Task-scheduling. The proper scheduling of tasks among appropriate Fog nodes, considering the energy efficiency and reliability in terms of failure rate, is the main challenge for researchers. Many task scheduling algorithms have been proposed in the literature for energy efficiency and makespan; however, very little attention is paid to reliability. The high computational time of the task scheduling algorithm is another crucial aspect of these algorithms. This paper proposes a Reliable Hybrid Pareto-based Multi-objective (RHPMO) Task Scheduling approach based on metaheuristics. It combines two advanced metaheuristic techniques, named JAYA and Genetic Algorithm, to perform optimal task scheduling based on the Pareto front and explore the best global solution. A multi-objective function is formulated to minimize makespan, energy, and failure rate, thereby increasing the efficiency and reliability of task scheduling. Extensive experimentation is conducted on MATLAB R2023a on an Intel i3, 8 GB RAM. The simulation experiment results of the proposed hybrid approach are compared with the various metaheuristic approaches like JAYA, Genetic Algorithm, Particle Swarm Optimization, Bees Algorithm, and Ant Colony Optimization, and hybrid GA and PSO. The proposed approach surpasses various state-of-the-art studies and demonstrates an impressive improvement of 68.91% in computational time, 42.48% in makespan, 29.83% in energy consumption, and 12.58% in reliability, respectively.