The developing concepts in networks, such as software-defined networking (SDN), virtual network embodiments, network function virtualization (NFV), and end-to-end orchestration, have increased the complexity of network management significantly, to a level that reaches far beyond what the current network management tools can offer. These challenges are more apparent in 5G and 5G bearer networks. On the other hand, computing capabilities have become critical in 5G architectures to enable low-latency communications and edge computing. Beyond network applications, network functions, such as RU, DU, and CU in the ORAN-based 5G-RAN solution, will run on the available computing resources without the limits of the dedicated hardware. Therefore, the deep integration of networking and computation has become a common goal to support emerging internet applications and functions. The latest advancements in machine-learning (ML) technologies provide a plethora of tools to drive the innovation of networks in terms of network management and network operations. It is believed that more advanced machine-learning technologies will be deployed in networks to enable revolutions in network management and operations, with the available computation resources. In this Special Issue, we will focus on ML-enabled networking and computation and intend to explore novel networking and computation resource provision, dynamic network operations, and self-managed networks to pave the way toward autonomous networks.
Dr. Shuangyi Yan Prof. Dr. Yongli Zhao Dr. Xiaoliang Chen
Submission Deadline: July 31 2021 Link: https://www.mdpi.com/journal/photonics/special_issues/Machine_Learning_Era_Cloud_Network_Integration Filelink: Detailed description