# Predictive Migration Performance in Vehicular Edge Computing Environments
## 2 Related Work
To tackle the problem of not having enough resources in the MEC infrastructure to host all requested services, the authors in [9] propose a collaborative computation offloading and resource allocation optimisation scheme that utilises the cloud when the MEC servers do not meet the resource demand. For a comprehensive survey of resource management techniques in fog/edge computing, please refer to [10], where different approaches in terms of architecture, infrastructure, and algorithms are presented. The focus of this paper is on proactive mechanisms for resource management that are based on prediction of service requests and preemptive implementation of service actions so that the latency experienced is as low as possible. A number of different predictive approaches aiming to increase the resource management performance have been proposed. The authors in [11] provide a survey of artificial intelligence (AI) techniques used for computation offloading in edge computing. A special section is dedicated to the problem of offloading when the users represent a vehicular network, as well as problems such as a highly dynamic environment, intermittent connectivity, and the requirement of minimum latency. However, the techniques discussed mostly focus on the scheduling aspects--deciding when and where to do offloading and how much should be offloaded. For example, in [12], the authors propose a deep Q-network technique for task migration in order to help autonomous driving applications. Reinforcement learning is used to make decisions on when to migrate the task based on past experience, aiming to maximise the total sum of reward. A different approach is considered in [13], which proposes an intelligent task prediction and computation offloading, where a deep learning algorithm based on long short-term memory (LSTM) is used for prediction of computing tasks. The decision on whether to offload is based on minimising the delay, which is represented as a function of the predicted task size, computing resources on the user side, and computing resources in the edge. A similar objective function is used for task migration in the case when there is a problem with the execution of the task in the edge. However, no matter the technique used for learning and predicting, there is always a degree of error in the prediction, which might introduce adverse effects in the service levels. In this work, we aim to investigate the performance when using proactive algorithms for service migration in which AI techniques are used to predict user mobility and, hence, to foresee the need for service migration. For a visualisation of the problem, please refer to Figure 3. The main idea of the proactive service migration is to respond to the predicted handover event when the vehicle switches from one base station to another, and to preemptively start a live migration process so that the change of base station does not impact the latency when the vehicle accesses the hosted MEC service. Of course, the risk of mobility prediction is that it is not guaranteed that it will be 100% correct at all times, and there might be cases when latency will be introduced into the system because of an incorrect prediction, in which case the service migration is done as a reactive instead of proactive process. A similar consideration is analysed in [14], where a Glimpse mobility prediction model is proposed to predict the next user locations using deep reinforcement learning. The results presented for a small-scale scenario show that the minimum latency is observed in the situations where the chosen target MEC server is located close to the user. On the other hand, in [15], the authors argue that using simpler prediction mechanisms that focus on the prediction of handover events instead of the prediction of the detailed user mobility can outperform the more complex AI-based techniques, which require training and a lot of preprocessing.