Data-driven Resource Management in a 5G Wearable Network using Network Slicing Technology

The rapid development of the wearable technology brings an explosive growth of wearable devices and imposes a new challenge to the current network. This is because the wearable devices require real-time interaction and data processing. To cope with this challenge and to realize reasonable utilization of resources, this paper first introduces the network slice-based 5G wearable networks, including the 5G ultra-dense cellular network, the edge caching, and the edge computing. Then, in order to realize the service aware and efficient management of network slicing resources, we propose a data-driven resource management framework which includes the service cognitive engine, the resources cognitive engine, and the global cognitive engine. Furthermore, through information perception, analytical prediction, policy decisions, and performance evaluation, the data-driven resources management method is realized. Finally, we set up a real testbed and conduct a related experiment. The experimental results show that the data-driven resources management scheme can realize the service-aware resources allocation and improve the utilization ratio of resources.