Data Driven Service Orchestration for Vehicular Networks

As technology progresses, cars can not only be considered as a transportation medium but also as an intelligent part of the cellular network that generates highly valuable data and offers both entertainment and security services to the passengers. Therefore, forthcoming 5G networks are said to enhance Ultra-Reliable Ultra-Low-Latency that will allow for a new breed of services that will disrupt the industry as we know it today. In this work, we devise a unique fusion of Deep Learning based mobility prediction and Genetic Algorithm assisted service orchestration to retain the average service latency minimal by offering personalized service migration, while tightly packing as many services as possible in the edge of the network, for maximizing resource utilization. Through an extensive simulation based on real data, we evaluate the proposed mobility orchestration combination and we find gains in low latency in all examined scenarios.

Automated summary

This study examines the combination of deep learning-based mobility prediction and genetic algorithm-assisted service orchestration to reduce service latency and maximize resource utilization. Through simulation experiments, it was found that gains in low latency were achieved in all scenarios examined.