Resource Allocation over Cloud-Fog Framework Using BA

Edge computing or fog computing (FG) are introduced to minimize the load on cloud and for providing low latency. However, FG is specified to a comparatively small area and stores data temporarily. A cloud-fog based model is proposed for efficient allocation of resources from different buildings on fog. FG provides low latency hence, makes the system more efficient and reliable for consumer’s to access available resources. This paper proposes an cloud and fog based environment for management of energy. Six fogs are considered for six different regions around the globe. Moreover, one fog is interconnected with two clusters and each cluster contains fifteen numbers of buildings. All the fogs are connected to a centralized cloud for the permanent storage of data. To manage the energy requirements of consumers, Microgrids (MGs) are available near the buildings and are accessible by the fogs. So, the load on fog should be balanced and hence, a bio-inspired Bat Algorithm (BA) is proposed which is used to manage the load using Virtual Machines (VMs). Service broker policy considered in this paper is closest data center. While considering the proposed technique, results are compared with Active VM Load Balancer (AVLB) and Particle Swarm Optimization (PSO). Results are simulated in the Cloud Analyst simulator and hence, the proposed technique gives better results than other two load balancing algorithms.