Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles

Rhodney Simões, Kelvin Dias and Ricardo Martins
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Rhodney Simões: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil
Kelvin Dias: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil
Ricardo Martins: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil

Future Internet, 2021, vol. 13, issue 11, 1-24

Abstract: The expected huge amount of connected cars and applications with varying Quality of Service (QoS) demands still depend on agile/flexible networking infrastructure to deal with dynamic service requests to the control plane, which may become a bottleneck for 5G and Beyond Software-Defined Network (SDN) based Internet of Vehicles (IoV). At the heart of this issue is the need for an architecture and optimization mechanisms that benefit from cutting edge technologies while granting latency bounds in order to control and manage the dynamic nature of IoV. To this end, this article proposes an autonomic software-defined vehicular architecture grounded on the synergy of Multi-access Edge Computing (MEC) and Network Functions Virtualization (NFV) along with a heuristic approach and an exact model based on linear programming to efficiently optimize the dynamic resource allocation of SDN controllers, ensuring load balancing between controllers and employing reserve resources for tolerance in case of demand variation. The analyses carried out in this article consider: (a) to avoid waste of limited MEC resources, (b) to devise load balancing among controllers, (c) management complexity, and (d) to support scalability in dense IoV scenarios. The results show that the heuristic efficiently manages the environment even in highly dynamic and dense scenarios.

Keywords: dynamic allocation of controllers; SDN; NFV-based MEC architecture; IoV; high-density scenarios (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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