# Predictive Migration Performance in Vehicular Edge Computing Environments
## 2 Related Work  
A number of studies [8; 9; 10] have researched the problem of offloading tasks to the edge, focusing on various aspects of the problem, such as when to offload and when not to offload or partial or full offloading; a number of algorithms [11; 12; 13] have been proposed to deal with the decision-making problem of where to place the service and when and where to migrate a service in order to maintain the agreed-upon service level. Lately, these MEC resource management approaches have turned towards proactive methods that use predictive techniques [14; 15] to preemptively reserve the resources in the MEC infrastructure and, thus, guarantee continuous service delivery with minimum delay. The proactive behaviour is based on learning the end device's patterns and predicting its future requirements. This preemptive resource management in terms of service placement and migration involves learning the mobility patterns and responding to the changes imposed by the predicted new locations. The MEC reference architecture, as specified by ETSI [16], defines three main components that enable the management and hosting of user services, i.e., MEC applications. Starting from the hosts that comprise the MEC computing infrastructure, each mobile edge host is managed via the mobile edge platform that is in charge of the virtual resources offered by the edge host. A group, or cluster, of edge hosts, i.e., local MEC datacentre, is managed by the mobile edge platform manager, which decides how to best use the resources in the cluster in order to provide the highest efficiency in terms of service performances and energy consumption. The heart of the system is implemented in the mobile edge orchestrator, which manages the complete MEC service lifecycle. It receives all end-user equipment requests for service provisioning and management and decides which platform manager will be in charge of fulfilling a given request. Hence, the orchestrator has a top-level view of all MEC resources and their current states combined with the information about the past and current positioning of the end-user equipment based on the base stations used for communication. The main goal of the MEC orchestrator is to enable highly effective resource management of the MEC hosts so that the MEC services provided to the user adhere to the requirements in terms of processing power, storage, bandwidth, and latency. The resource management activities can be divided into two categories: placement of newly requested services where the orchestrator needs to decide on the optimal location, i.e., MEC host, where a new MEC application will be instantiated using available virtual resources, and migration of existing services to a different MEC host so that the service performance requirements are kept optimal during the service lifetime. In the recent literature, there have been many different proposals on how to tackle the complexities of the resource management problem, which has been shown to be NP-hard. Focusing on scenarios related to using the MEC infrastructure to host vehicular services, existing methods include augmenting the MEC infrastructure with next-generation networking technologies, such as software-defined networks (SDNs) and network function virtualisation (NFV), so that network agility can be achieved to match the dynamics of the vehicular network. An example is presented in [8], where the authors introduce SDN-enabled resource management to support the heterogeneous quality of service re quirements. They investigate resource management by balancing resource usage under the constraints of computing, storage, and bandwidth. The problem is formulated as an optimisation framework that focuses on maximising network utility, which is represented as a logarithmic function.