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.
