# Predictive Migration Performance in Vehicular Edge Computing Environments
## 3 Proactive Approach to MEC Resource Management
In this paper, we consider the delay as the network time required to communicate with the MEC host where the virtual service is allocated with the network connection of the access point. The values computed based on the fat tree network delay available in CloudSim are: 1.57 ms for a communication through the edge layer, 2.45 ms for a communication through the aggregation layer, and 2.85 ms for a communication through the fat tree core layer. In order to study the impact of prediction accuracy impact on the service delay, we depicted the relation of the average delay per edge service in contrast to the workload expressed as the number of vehicles in Figure 7. We can observe in Figure 7a that the prediction accuracy does not have much of an effect when MEC resources are scarce and especially when the service demand is high enough to leave a small idle margin after the consolidation process. Meanwhile, the delay differentiation is clearer in less dense scenarios. Improving the hardware infrastructure at the MEC layer allows us to clearly distinguish the effects on latency of having a range of reliability in prediction. Figure 7e shows that there can be more than 1.2 ms difference in delay between having prediction accuracies of 80% and 100%. In other words, when we compare it with the value of a delay per hop, this additional delay can be considered as adding an additional hop in the network. ### Study of the Relation between Delay and Prediction Accuracy

We can have a closer look at the effects of the accuracy level of the prediction in the resulting MEC service delay by showing the difference in percentage with the correct prediction delay versus the inaccuracy level in another set of images. In Figure 8, the relation of both errors is depicted; in the \(y\) axis, the difference in percentage between the delay obtained with 100% correct prediction and the delay obtained with 95%, 90%, 85%, and 80% prediction accuracy is represented, and the \(x\) axis represents the inaccuracy percentage of the prediction. We can observe that the relation of both metrics is almost linear when the vehicle density in the scenario is moderate and there are enough resources at the MEC

Figure 6: Number of migrations of edge services considering their distance to the MEC host that is providing the service. infrastructure. This relation is consistent and shows that when the infrastructure resources are not congested, an improvement in delays of almost 80% is obtained when the prediction inaccuracy is non-existent. Therefore, we can confirm that a good prediction related to service migration would have a consistent effect on the MEC service delay. However, we can also observe that even the best prediction cannot help much in reducing the delay when there is a lack of infrastructure resources for a defined workload. Figure 7: Average delay per MEC service considering the prediction accuracy. ## 6 Conclusions

Autonomous driving, automated traffic management, and other advanced vehicular services put a tremendous strain on the onboard computing power in vehicles. The use of a 5G network augmented with mobile edge computing servers can help alleviate this problem by providing additional virtual resources distributed in MEC server hosts located next to the 5G base stations. However, due to the high mobility of vehicles, intelligent resource management is needed to implement an efficient service migration in order to continuously maintain low latency while accessing the MEC services. For these purposes, a proactive approach to service migration based on employing predictive algorithms is necessary. In this paper, we have described how a hierarchical community-based resource management technique can be transformed from reactive to proactive behaviour by introducing a mobility prediction module. We then used this new implementation to analyse the performances of the MEC services in terms of latency experienced in a realistic urban vehicular scenario.