HAPTA: Adaptive Learning Based Horizontal Computation Offloading over RSUs in VEC

The rapid evolution of vehicular edge computing (VEC) has made task offloading to roadside units (RSUs) essential for addressing the computational demands of modern vehicular applications. However, efficiently partitioning and scheduling tasks across RSUs in dynamic environments remains challenging. This paper introduces a novel algorithm, HAPTA (Horizontal, Adaptive Learning-based, Partitioned, Timeslot-based Algorithm), a time-slot based task partitioning and offloading approach using the Upper Confidence Bound (UCB) algorithm, to adaptively allocate tasks to RSUs. We consider task offloading horizontally across RSU nodes. Unlike static or heuristic-based scheduling methods, the proposed HAPTA framework dynamically selects RSUs with optimal resource availability while considering vehicular mobility and task deadlines. The framework supports splitting tasks into subtasks, enabling efficient resource utilization and meeting stringent latency constraints. Experimental evaluations on real-world vehicular datasets demonstrate that the HAPTA approach outperforms previously introduced heuristic-based methods, achieving higher task completion rates and lower latency, particularly in high-density scenarios.