PROJECT TITLE :

Pricing and Resource Allocation Optimization for IoT Fog Computing and NFV: An EPEC and Matching Based Perspective

ABSTRACT:

The Internet of Things (IoT) is experiencing explosive growth on a global scale, with more and more devices connecting to it every day. Distributed fog computing deployments at the network edge can make computing resources available to users in networks of the next generation, particularly for latency-sensitive applications. In addition, the disparate demands placed on networks of the fifth generation (also known as 5G) necessitate the virtualization of network functions, which is referred to as network function virtualization (NFV). As a result, developing a resource allocation framework for IoT that is integrated with NFV and fog computing is of the utmost significance. As a result, in this paper, we model the interactions between data service operators (DSOs) and authorized data service subscribers (ADSSs) as an equilibrium problem with equilibrium constraints (EPEC), and we use the alternating direction method of multipliers (ADMM) as a large-scale optimization tool to obtain solutions. DSOs are organizations that provide data services, and ADSSs are individuals who subscribe to those data services. This leads to the optimization of resource pricing for the DSOs and the amount of resources that will need to be purchased by the ADSSs as a result of the process. In addition, we propose a model that is based on many-to-many matching in order to distribute the resources of the fog nodes (FN) in accordance with the VNF resource requirements of the ADSSs. The results of our simulations demonstrate that the approach that we have proposed is effective in achieving efficient resource allocation in NFV-enabled IoT fog computing.