  Multi-access edge computing (MEC) is a key enabler to reduce the latency of
vehicular network. Due to the vehicles mobility, their requested services
(e.g., infotainment services) should frequently be migrated across different
MEC servers to guarantee their stringent quality of service requirements. In
this paper, we study the problem of service migration in a MEC-enabled
vehicular network in order to minimize the total service latency and migration
cost. This problem is formulated as a nonlinear integer program and is
linearized to help obtaining the optimal solution using off-the-shelf solvers.
Then, to obtain an efficient solution, it is modeled as a multi-agent Markov
decision process and solved by leveraging deep Q learning (DQL) algorithm. The
proposed DQL scheme performs a proactive services migration while ensuring
their continuity under high mobility constraints. Finally, simulations results
show that the proposed DQL scheme achieves close-to-optimal performance.
