Abstract:
In recent years, Internet-connected devices have increased significantly. Technologies
such as the Internet of things and cloud computing are enabling more and more devices to
connect to the Internet. With the rise in the number of Internet-connected devices, new
computational paradigms such as edge computing and fog computing are emerging. All
these changes are making the network infrastructure very complex, dense and
heterogeneous. In this dynamically altering and growing scenario, existing traditional
network infrastructures are inadequate to fulfill the growing data requirements, the
network service providers need to update the infrastructure hardware and software
parameters dynamically. They need to manage co-operation, co-ordination and coexistence among diverse network types. For this, novel self configuring resource
management techniques are required. In this direction, we have presented novel methods
for allocating resources at different levels of network infrastructure where computational
resource optimization for IoT devices has been done. IoT device density is increasing and
the current philosophy of processing requests in the cloud is not appropriate for emerging IoT
domains such as health care and real time control. We have considered using a variety of
devices available at the network access layer. This includes the devices voluntarily given
by users, dedicated edge servers and cloud infrastructure. The proposed system learns the
optimal operating parameters during initial runs. Using the knowledge acquired in the
learning phase, an integer linear programming problem is formulated to minimize the
meantime to complete the request for all the IoT nodes. The solution to the formulated
problem provides fair resource allocation for all the IoT nodes. Later, considering the
unreliable nature of the voluntary devices, the learning and formulation have been extended
to incorporate the probability of failure of these devices. A multi-objective optimization
problem has been formulated and solved using a genetic algorithm.
