Guest Editorial Leveraging Machine Learning in SDN/NFV-Based Networks

David S. L. Wei, Kaiping Xue, Roberto Bruschi, Stefan Schmid
2020 IEEE Journal on Selected Areas in Communications  
A KEY trend of current network evolution is in the direction of network softwarization and virtualization. These technological paradigms aim to enable a network to be programmable in a way that makes the network more flexible, scalable, and reliable, and in turn leads to agile service deployment and lower capital and operational expenses. So far, two related widely adopted solutions are software defined networks (SDN) and network function virtualization (NFV). There is one main difference
more » ... n these two new networking paradigms. SDN separates the control plane from the data plane through a well-defined programming interface, such that the centralized controller can have a complete view of the entire network, while NFV decouples network functions from dedicated physical equipment by means of virtualization technology, and runs the virtual network functions (VNFs) in the general purpose physical or virtual network appliances. Both approaches make the network programmable in order to have the aforementioned desired features. SDN and NFV do not depend on each other, and they actually complement each other. They can work well individually and can also work in tandem for performance reasons. Due to such advantages, both SDN and NFV have become key enabling technologies for 5G networks, and have also been used in a wide range of important areas including IoT, mobile edge computing, smart grid, cloud datacenters, and cognition-based networks. Although SDN and NFV facilitate the flexibility and scalability of network services and make the deployment of network services faster and cheaper, such software-based solution also introduces new problems, including throughput performance degradation and unstable jitter. More specifically, D. S. L. Wei is with the
doi:10.1109/jsac.2019.2959197 fatcat:rapy36t4njgxbgeezl4xe3egky