# Predictive Migration Performance in Vehicular Edge Computing Environments
## 1 Introduction  
Advances in vehicular networking in recent years have tremendously changed the requirements of next-generation networks. The communication patterns start from the ad hoc creation of dynamic vehicular networks (VANETs) in an infrastructure-less environment, but also include infrastructure-oriented communication, such as information exchange between vehicles and smart road infrastructure, or, lately, the emerging 5G ecosystem, which has created creates an opportunity for the development of so-called 5G-enabled vehicular communications and networking (VCN) [1]. In essence, the introduction of 5G offers the possibility to reach ultra-reliable, extremely low-latency, high-speed communication among a plethora of devices, with vehicles being one of the main use-case scenarios [2]. Since its introduction, 5G has involved not only changes in radio access technology, but drastic changes in the overall architecture of service delivery. In the new landscape, 5G Vehicle-to-Enerything (V2X) communications can be considered as an integral part of the advancements in autonomous driving. The autonomous driving use cases and requirements, defined as V2X phase 3, are being developed in the latest 5G Standard Release16 defined by the 3 GPP [3]. In addition to these standardisation efforts, the 5G Automotive Association has developed a roadmap for advanced driving use cases, connectivity technologies, and radio spectra related to the use of 5G for V2X communications [4]. In this ubiquitously connected environment supported by the 5G ultra-communication features, the number of possibilities and services that can be developed around the premise of autonomous driving is staggering. However, these go hand in hand with the demand for increased processing power that goes beyond the capabilities of the vehicle processing unit. Autonomous vehicles are expected to be able to deal with big data on the fly while being highly mobile. To overcome these challenges, the services and applications running on the vehicles need to rely on a mix of a local on-board processing and remote additional processing power [5]. To be able to provide these remote processing features, the 5G architecture is extended with support for multi-access edge computing (MEC). Using the possibilities for remote heavy weight processing, autonomous driving can be augmented with services such as automated traffic management, where a real-time analysis of the current traffic status in a whole area can be conducted to help the vehicle efficiently navigate to its destination. In parts of the literature, the specific use cases that focus on the integration of vehicular networks and MEC are known as vehicular edge computing (VEC) [6]. The MEC concept describes an architecture where the all-purpose processing and storage power of traditional cloud computing datacentres is relocated on the edge of the network. Thus, the 5G micro-base stations come equipped with a number of general-purpose virtualised servers to provide additional processing power requested by the users, as can be observed in Figure 1. In this way, even when the processing is partially or fully dislocated from the vehicle, the delay in obtaining the results is negligible due to the combination of the extreme low latency of the 5G network and the co-location of the processing power right next to the base station. By taking advantage of this opportunity, vehicular services practically have no limitations due to lack of resources. However, to be able to efficiently implement this scenario for 5G user equipment that is highly dynamic and mobile, as is the case with autonomous vehicles, there are some considerations that need to be taken into account [7]. The main problem in exploiting the MEC processing capabilities in vehicular environments is the frequent handover (or change of base stations) due to mobility. In other words, once the vehicle exits the coverage area of the base station with the co-located MEC servers that host those vehicle services, the premise of ultra-low latency becomes an issue.