Efficient Resource Utilization in Cloud-Fog Environment Integrated with Smart Grids

Abstract

The cloud and fog computing integration with Smart Grid (SG) improve the efficiency of SG. SG is a modern electricity network that improves performance, reliability, stability and energy consumption. The SG integration with cloud computing improves allocation of resources. Concept of fog computing is introduced to reduce the load on cloud and improve the allocation of resources. The fog provides the same services as the cloud. However, fog is closest to the end users that improve response time and resource utilization. Fog cover small area than cloud and store data temporarily, for permanent storage fog communicate with the cloud. The main features of fog are; location awareness, low latency and mobility. In this thesis, we presented cloud and fog based framework for information management. Fog computing makes the system efficient by using load balancing algorithm to allocate Virtual Machines (VMs). We have proposed a novel approach which is based on binary particle swarm optimization with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of search space for finding the optimal solution. BPSOSA is evaluated against round robin, odds algorithm and ant colony optimization. In terms of response time BPSOSA outperforms round vii robin, odds algorithm and ant colony optimization by 53.99 ms, 82.08 ms and 81.58 ms respectively. In terms of processing time BPSOSA outperforms round robin, odds algorithm and ant colony optimization by 52.94 ms, 81.20 ms and 80.56 ms respectively. Ant colony optimization has slightly better cost efficiency but the difference is insignificant.