IoT Ecosystem on Exploiting Dynamic VNF Orchestration and Service Chaining: AI to the Rescue?
Mahzabeen Emu, Salimur Choudhury · IEEE Internet of Things Magazine · 2020
An efficient automated virtual network function (VNF) deployment and service function chaining (SFC) can induce a significant improvement in the overall performance of various IoT services. Few concerns regarding the latency benefits, energy consumption expenditure, and migration costs are required to be taken into consideration collaboratively for the solution method to accommodate supreme privileges for both users and providers. However, most of the works existing in the literature emphasize these issues exclusively. Additionally, they focus on employing traditional mathematical programming-based approaches to find optimal solutions that are computationally expensive. Thus, state-of-the-art methods are infeasible and not prompt enough to provide real-time solutions for massive IoT services. In this article, we propose the utilization of different deep learning and reinforcement learning techniques (e.g., artificial neural networks, convolutional neural networks, deep Q-networks, and federated learning) for swift VNF orchestration and SFC. Moreover, we identify some challenges and their potential solutions associated with these sophisticated learning models. Then we present some simulation results on a VNF deployment case study demonstrating that deep learning techniques can be a significant breakthrough with promising potential to resolve most of the mentioned concerns incorporated with the VNF orchestration and SFC generation problem.