# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 3 System Structure Overview  
We consider a vehicular cloud computing architecture in Figure 1, which includes vehicles in dynamic traffic flow, virtual RSUs distributed in urban areas, and a cloud platform to support on a large scale. Initially, the traffic flow map in urban areas can be achieved through the distributed traffic monitoring or a third party Internet map service provider. With the traffic flow map, the virtual RSU scheduling can be implemented by cloud platform for a large scale content delivery coverage. The RSUs cache the file items that can be downloaded by the vehicles nearby. There are a large set of files in total, which are all stored in the cloud platform as backups. Meanwhile, the RSU storage capacity is limited, and cache items need to be updated according to the replication strategy. Each RSU receives the requests from the traffic flow in its coverage area, and feeds back the request demand according to its local replication. From the viewpoint of a specific vehicle, its request demand can be fulfilled by the RSUs passing by; otherwise, it will resort to the support of cloud platform or vehicle-to-vehicle communications. The RSU scheduling strategy includes RSU allocation and cache replication. Specifically, the RSU allocation determines where to allocate the RSUs, and the content replication determines what to cache in these allocated RSUs.  
**RSU Allocation**. Given the traffic flow map, RSU allocation will select an RSU distribution to balance the system cost and performance. As we assume a virtual RSU based on network function virtualization (NFV) technology, the RSU can be started or terminated dynamically according to the time-varying traffic flows. Intuitively, provisioning more RSUs would bring higher service coverage, and also leads to higher system cost. Through V2V and V2I communications in VANETs, the RSUs are allocated to facilitate the content delivery under the cost constraints. In other words, given a limited number of RSUs, we need to find an appropriate RSU placement to improve the connectivity of the whole system.  
**Cache Replication**. After the RSUs are allocated, the cache replication strategy refers to the file item management in the local storage of RSUs. As the RSUs usually have limited storage capacity, the content replication needs to be updated according to the various request demands of vehicles passing by. Naively caching the most popular item content can hardly guarantee the efficiency of the whole system, as the RSUs are usually allocated along the road and accessed by the passing-by vehicles in sequence. Therefore, we need to consider both the request demand and RSU distribution, and explore the service scheduling strategy among the neighboring RSUs.  
We present the details of the RSU scheduling strategy design procedures in Figure 2, including the RSU allocation and cache replication. Initially, we consider the dynamic environment with time-varying  
Figure 1: A road segment with a bidirectional road.  
traffic conditions and various request demands. According to the traffic conditions, the RSU allocation strategy is designed to improve the service capacity with a limited number of RSUs. According to the RSU distribution and the popularity of the request demand, the cache replication is designed to improve the service efficiency with storage capacity constraint.