  In this work, we investigate an online service management problem in
vehicular edge computing networks. To satisfy the varying service demands of
mobile vehicles, a service management framework is required to make decisions
on the service lifecycle to maintain good network performance. We describe the
service lifecycle consists of creating an instance of a given service
(\textit{scale-out}), moving an instance to a different edge node
(\textit{migration}), and/or termination of an underutilized instance
(\textit{scale-in}). In this paper, we propose an efficient online algorithm to
perform service management in each time slot, where performance quality in the
current time slot, the service demand in future time slots, and the minimal
observed delay by vehicles and the minimal migration delay are considered while
making the decisions on service lifecycle. Here, the future service demand is
computed from a gated recurrent unit (GRU)-based prediction model, and the
network performance quality is estimated using a deep reinforcement learning
(DRL) model which has the ability to interact with the vehicular environment in
real-time. The choice of optimal edge location to deploy a service instance at
different times is based on our proposed optimization formulations. Simulation
experiments using real-world vehicle trajectories are carried out to evaluate
the performance of our proposed demand-prediction based online service
management (DOSM) framework against different state-of-the-art solutions using
several performance metrics.
