Load balancing using a Hybrid Hydrozoan and Sea Turtle Foraging Optimization Algorithm in FOG Computing

Abstract

Internet of Things (IoT) is network of huge and intricate devices, wherein fog computing systems is significantwith the intention of handling the data flow of such huge and intricate network. Customarily, in fog computing environments, load balancing delinquent ariseswhen a large count of new IoT user requests are linked with specific fog nodes. So, a well-organized load balancing tactic is needed in fog computing. Therefore, in this manuscript, a Hybrid Hydrozoan and Sea Turtle Foraging optimization Algorithmsbased improved energy efficient Resource AllocationMethod to load balance in fog computing (IDRAM-LB-FC-Hyb-HySTFOA)is effectively proposed for reducing task waiting time, Load Balancing Rate, Scheduling Time, Delay and Energy Consumption. The evaluation metrics, like Response Time, Load Balancing Rate, Scheduling Time, Delay,and Energy Consumption are analyzed. Then the simulation performance of the Improved energy efficient resource allocation method for load balancing in Fog computing using Hybrid Hydrozoan and Sea Turtle Foraging optimization Algorithms(IDRAM-LB-FC-Hyb-HySTFOA) provide 32.82% and 25.32% low delay, 38.22% and 25.46% low energy consumption compared with the existing methods, like dynamic resource allocation method based load balancing using genetic algorithm in fog computing environment (DRAM-LB-FC-GA) and Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method (EDRAM-LB-FC-PSOA).