Efficient load distribution in heterogeneous vehicular networks using hierarchical controllers

Abstract

Vehicle movement poses significant challenges in vehicular networks, often resulting in uneven traffic distribution. Fog computing (FC) addresses this by operating at the network edge, handling specific tasks locally instead of relying solely on cloud computing (CC) facilities. There are instances where FC may need additional resources and must delegate tasks to CC, leading to increased delay and response time. This work conducts a thorough examination of previous load balancing (LB) strategies, with a specific focus on software-defined networking (SDN) and machine learning (ML) based LB within the internet of vehicles (IoV). The insights derived from this research expedite the development of SDN controller-based LB solutions in the IoV network. The authors proposes the integration of a local SDN controller (LSDNC) within the FC tier to enable localized LB, addressing delay concerns. However, the information will be available to the main SDN controller (MSDNC) too. The authors explore the concept mathematically and simulates the formulated model and subjecting it to a comprehensive performance analysis. The simulation results demonstrate a significant reduction in delay, with a 125 ms difference when 200 onboard units (OBUs) are used, compared to conventional software-defined vehicular networks (SDVN). This improvement continues to increase as the number of OBUs grows. Our model achieves the same maximum throughput as the previous model but delivers faster response times, as decisions are made locally without the need to wait for the main controller.