Cost-effective task partial offloading and resource allocation for multi-vehicle and multi-MEC on B5G/6G edge networks

The Beyond 5th Generation/6th Generation (B5G/6G) wireless communication technology, characterized by ultra-low latency and ultra -multiple connections, and B5G/6G edge networks provide a new approach to solve delay-sensitive and computation-intensive vehicle applications in Intelligent Transportation Systems (ITS). However, due to the high mobility of vehicles, it becomes challenging to provide mobility-enabled resource management and delivery tasks from multiple vehicle users to Base Station (BS) in B5G/6G edge networks. Therefore, we investigate a multi-vehicle user and multi-BS collaborative offloading system in B5G/6G edge networks, and propose a joint optimization scheme for collaborative offloading, unequal task splitting and CPU resource allocation. In this scheme, tasks from vehicle users can be partially offloaded to associated BS, and can be further split and offloaded to adjacent BS with multi -hop network technology, thereby minimizing the weighted sum of latency and energy consumption. Thus, a Mixed Integer Nonlinear Optimization Problem (MINLP) is constructed. To address this issue, we propose a two-level alternating iterative framework based on a two-layer co-offloading architecture and Sequential Quadratic Programming algorithm (SQP). In the upper level, we introduce a multi-BS collaboration algorithm at the edge layer and develop a collaborative offloading strategy between vehicle users and the edge layer, utilizing Game Theory (GT). In the lower level, based on the SQP algorithm, the optimal task splitting ratio and the optimal CPU frequency allocation strategy for each vehicle user task are solved. Simulation results demonstrate that the proposed algorithm not only effectively reduces system costs, but also excels in reducing the system latency or energy consumption when considered separately.