In vehicular edge computing, vehicles move along the road and request the services from the nearest edge servers with low latency. Due to the limitation of computation capacity of vehicular devices, the services should be offloaded on RSUs equipped with edge servers to provide service with low latency. Noticed that the location of service offloading may affect the service requesting delay directly, and it may exist some interrelationship between interacting services; all of these are rarely considered in recent studies. To address such problems, we propose a collaborative service offloading approach with deep reinforcement learning in vehicular edge computing named CSO-DRL. Our approach first divides the road segments by k-means-based algorithm through analyzing the trajectory data of vehicles, and then the offloading location is determined by observing the vehicle running status. Secondly, the interacting services are discovered by a parallel frequent pattern-based algorithm efficiently. Furthermore, the collaborative service offloading algorithm is presented by the DDPG model for offloading the interacting services, which can minimize the service requesting delay and data communication delay between interacting services. Finally, the efficiency of the algorithm is evaluated by real-world data-based simulation experimental evaluations. The results show our algorithm can obtain a lower delay than other baseline algorithms in searching for the optimal service offloading strategy.
In vehicular edge computing, the low-delay services are invoked by the vehicles from the edge clouds while the vehicles moving on the roads. Because of the insufficiency of computing capacity and storage resource for edge clouds, a single edge cloud cannot handle all the services, and thus the efficient service deployment strategy in multi edge clouds should be designed according to the service demands. Noticed that the service demands are dynamic in temporal, and the inter-relationship between services is a non-negligible factor for service deployment. In order to address the new challenges produced by these factors, a collaborative service on-demand dynamic deployment approach with deep reinforcement learning is proposed, which is named CODD-DQN. In our approach, the number of service request of each edge clouds are forecasted by a time-aware service demands prediction algorithm, and then the interacting services are discovered through the analysis of service invoking logs. On this basis, the service response time models are constructed to formulated the problem, aiming to minimize service response time with data transmission delay between services. Furthermore, a collaborative service dynamic deployment algorithm with DQN model is proposed to deploy the interacting services. Finally, the real-world dataset based experiments are conducted. The results show our approach can achieve lowest service response time than other algorithms for service deployment.
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