Dynamic Resource Allocation for Cloud-Edge Collaboration Offloading in VEC Networks With Diverse Tasks

In vehicular edge computing (VEC) networks, vehicle terminal (VT) typically offloads tasks to road side units (RSUs) equipped with edge servers to obtain service with low latency. However, the lack of global information and time-varying nature of VEC networks present challenges to make effective resource allocation decisions under long-term constraints. Motivated by this, we aim to investigate a dynamic resource allocation scheme with diverse tasks. We formulate an optimization problem to minimize average task delay under long-term constraints of energy consumption and system cost for cloud-edge collaboration offloading. For the coupling of resource allocation decisions between different time slots, we propose a Lyapunov online resource allocation (LORA) algorithm. LORA transforms the formulated problem into a problem of minimizing the upper bound of the drift-plus-penalty function. We then decompose the latter into multiple subproblems and provide the corresponding algorithms for solving them separately. Experimental results show that our proposed LORA reduces energy consumption and system cost by 10.1% and 4.2%, respectively, compared to DO, and reduces average task delay by 15.1% compared to ECSCO.