Efficient Resource Allocation Algorithm for Fog of IoT

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.