SDN-Driven Delay Minimization for Task Offloading in VECN Using Particle Swarm Optimization
Md. Musfiq, Aktaruzzaman Kowshik, Md. Mahdizzaman Utsha, Jargis Ahmed, Md. Ahsan Habib Tareq · 2024
In Vehicular Edge Computing (VEC), efficient task offloading is essential for reducing latency and enhancing system performance, especially in resource-constrained environments. In this regard, Roadside Units (RSUs) can provide computation resources to vehicle users to execute offloaded tasks. However, RSUs are resource-constrained and could fail if many vehicular users offload tasks to certain RSUs. Hence balancing the load and distributing users among RSUs is required. In this paper, we considered a Mobile Edge Computing server (MEC) acting as an SDN that can observe the situation of the subordinate RSUs and map RSUs and users for computation offloading considering the delay constraint of the users. Due to the NP-hard nature of the formulated problem, we presented a meta-heuristic algorithm called Delay-aware Particle Swarm Optimization for task offloading (DAPSO). Where MEC as SDN will solve the task placement problem for vehicular users and direct users to offload tasks to the best possible RSU or the MEC server. Our experimental results demonstrate that the proposed DAPSO approach significantly reduces overall system delay and improves overall system efficiency compared to existing literature works.