Metaheuristic Optimization Technique for Load Balancing in Cloud-Fog Environment Integrated with Smart Grid

Energy Management System (EMS) is necessary to maintain the balance between electricity consumption and distribution. The huge number of Internet of Things (IoTs) generate the complex amount of data which causes latency in the processing time of Smart Grid (SG). Cloud computing provides its platform for high speed processing. The SG and cloud computing integration helps to improve the EMS for the consumers and utility. In this paper, in order to enhance the speed of cloud computing processing edge computing is introduced, it is also known as fog computing. Fog computing is a complement of cloud computing performing on behalf of cloud. In the proposed scenario numbers of clusters are taken from all over the world based on six regions. Each region contains two clusters and two fogs. Fogs are assigned using the service broker policies to process the request. Each fog contains four to nine Virtual Machines (VMs). For the allocation of VMs Round Robin (RR), throttle and Ant Colony Optimization (ACO) algorithms are used. The paper is based on comparative discussion of these load balancing algorithms.