# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 1 Introduction  
Large-scale data dissemination, e.g., multimedia streaming applications, has become one of the major tasks of vehicular ad hoc networks (VANETs) [1]. Much effort is required to deal with some issues in vehicular environments, such as the fast changing topology, the shortened intermittent connectivity, the unique set of applications, and the harsh propagation conditions [2; 3; 4]. Such distinguishing features demand the design of new networking solutions [5; 6]. As an extension of mobile cloud computing, vehicular cloud computing (VCC) is introduced as a new paradigm, which aims to make an efficient use of resources available in vehicles, such as computing, sensing, and communication to provide useful services [7; 8]. Unlike the traditional Internet cloud, which is created and maintained by a cloud provider, the vehicle cloud is temporarily created by interconnecting resources available in the vehicles and roadside units (RSUs). The location-based and time-dependent vehicular applications could potentially benefit from the in-network and decentralized data caching and replication mechanisms [9; 10; 11]. A resource-constrained RSU, joining the cloud as a front-edge node, can be a good candidate for the service of content delivery. Given the RSUs as the edge nodes along the road, these distributed nodes can constitute a fog computing environment, which can connect the dynamic traffic flow with a remote cloud to facilitate the content delivery in urban areas. Due to the intermittent connection, content delivery in VCC is particularly susceptible to the time-varying traffic condition, as well as the diverse request demand [12]. In order to improve the efficiency for real-world implementation, we have to consider a dynamic scenario in which the trafficcondition and request demand cannot be predicted for the next time slot. It is different from the static scenario, in which all the statistics can be known in advance and the global optimization can be achieved. In the dynamic environment, the adaptive solutions have to be implemented according to the current conditions for the services in the following time slots. To deal with this problem, infrastructure based networks hold a significant advantage: access points allow optimal scheduling of network resources distribution in a relatively simple manner, at the cost of RSU deployment throughout the intended coverage area [13; 14; 15; 16]. Consequently, the efficiency of content delivery strategy is largely influenced by the provisioning location and the replication content of these distributed RSUs. In this paper, we consider a cloud-based architecture, in which the virtual RSUs (denoted as RSUs for short in the following paper) can act as the front edge nodes of the cloud platform. We will consider the RSU-level cooperation in the dynamic environment, which refers to not only the traffic condition but also the request demand distributions. Specifically, we will explore the service efficiency among multiple RSUs along a road segment with bidirectional vehicle traffic flow. To deal with the vehicle traffic variation, we present a bidirectional allocation algorithm to select the bottleneck with time-varying link capacity. In addition, we further present a two-layer model and explore the replication strategy in a timely manner to update the content replication for the request demand diversity. The contributions of this paper are listed as follows:  
1. First, we present a system design for the content delivery service in the urban area, including the RSU allocation and cache replication. The system structure is explored to organize comprehensive factors and analyze the potential connections between them. 2. Second, as we have known, this is the first paper to explore the RSU allocation in a road segment with bidirectional traffic flows. In addition, we extend our analysis about the cache replication from a single road segment scenario to an urban area scenario. 3.