  Offloading time-sensitive, computationally intensive tasks-such as advanced
learning algorithms for autonomous driving-from vehicles to nearby edge
servers, vehicle-to-infrastructure (V2I) systems, or other collaborating
vehicles via vehicle-to-vehicle (V2V) communication enhances service
efficiency. However, whence traversing the path to the destination, the
vehicle's mobility necessitates frequent handovers among the access points
(APs) to maintain continuous and uninterrupted wireless connections to maintain
the network's Quality of Service (QoS). These frequent handovers subsequently
lead to task migrations among the edge servers associated with the respective
APs. This paper addresses the joint problem of task migration and access-point
handover by proposing a deep reinforcement learning framework based on the Deep
Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of
communication and computation of APs is proposed to minimize computational
load, service latency, and interruptions with the overarching goal of
maximizing QoS. We implement and evaluate our proposed framework on simulated
experiments to achieve smooth and seamless task switching among edge servers,
ultimately reducing latency.
