# Analysis of Mobile Edge Computing for Vehicular Networks +
## 2 CAMEVAN: Contextual Architecture for Mobile Edge-Computing in Vehicular Networks  
In this work, we propose a contextual architecture for mobile edge-computing in vehicular networks (CAMEVAN) that seeks to delegate computational tasks in a more efficient way. Our architecture considers a variety of parameters when deciding where to send a request. Factors include: computational complexity, memory impact, data size, and delay. Real-time decisions are made for each job that take into account each factor and examine available resources to determine an optimal location for task completion. Our architecture works by handling computational tasks in real-time and making a decision on which available resources the task should be executed. It should be noted that our architecture is designed toward computationally intensive tasks and does not generate requests for data or other trivial requests to remote resources. The types of tasks we target are those that will have noticeable effect on CPU usage and network performance. These are tasks that wish to perform some type of data processing, or execute a computationally intensive task. In the case of tasks involving large amounts of data that needs to be processed, pre-processing or data-reduction can be delegated to the less powerful edge nodes to reduce the amount of data that must be transmitted to a more capable remote resource. Figure 1 shows an overview of the layers of our the CAMEVAN architecture. In the network edge layer, we assume that we have computationally-enabled edge devices that have the ability to perform modest computational tasks. In our architecture, we propose computationally-enabled RSUs (CERSUs) that act as the base station for V2I communication, but can also provide limited computational resources. In this layer, we also include edge servers, that contain computational resources closer to the network edge. The final layer is the remote resource layer, which represents any remote application server, web server, or ITS application server. The overall goal of the architecture is to minimize the distance any request or data must travel over the network. The delay for a computational request could be reduced if the edge resource possesses reasonable processing power and is geographically closer to the requesting node. Given that edge resources will have limited capabilities and be required to support a large number of users, we must define a method of ranking all available resources in real-time. Any benefit in reduced communication delay would be irrelevant if the edge resource is near full-utilization and a distant resource is idle. To measure the real-time utilization of computational resources, our architecture queries available resources and simultaneously measures the round-trip communication delay between the requesting node and resource. Given the recent enough statistics, we can then rank each resource and provide a recommendation for a given task. In the CAMEVAN architecture, the task manager shown in Figure 2 is responsible for querying available resources and delegating tasks. Depending on the network configuration and type of computational resources available, the task manager could reside on each computational node or at end nodes only. For our case, we assume that the task manager exists at the end nodes of the network and delegates tasks to local and remote resources. Local resources are computationally-enabled edge nodes while remote resources are geographically distant servers. The task manager takes into account priority of the task and expedites execution when the given request is  
Figure 1: Contextual architecture for mobile edge-computing in vehicular networks (CAMEVAN) architecture. a real-time application. These requests are delegated to default resources that can be both local and remote depending on the task complexity. Less time-critical tasks, along with those involving larger amounts of data are delegated based on a status query of available resources.