@INPROCEEDINGS{Sacc2106:Control,
AUTHOR="Alessio Sacco and Flavio Esposito and Guido Marchetto",
TITLE="On Control and Data Plane Programmability for {Data-Driven} Networking",
BOOKTITLE="2021 IEEE 22nd International Conference on High Performance Switching and
Routing (HPSR) (IEEE HPSR'21)",
ADDRESS="Paris, France",
DAYS=6,
MONTH=jun,
YEAR=2021,
KEYWORDS="control plane; network programmability; machine learning",
ABSTRACT="The soaring complexity of networks has led to more and more complex methods
to manage and orchestrate efficiently the multitude of network
environments. Several solutions exist, such as OpenFlow, NetConf, P4, DPDK,
etc., that allow network programmability at both control and data plane
level, driving innovation in many focused high-performance networked
applications. However, with the increase of strict requirements in critical
applications, also the networking architecture and its operations should be
redesigned. In particular, recent advances in machine learning have opened
new opportunities to the automation of network management, exploiting
existing advances in software-defined infrastructures. We argue that the
design of effective data-driven network management solutions needs to
collect, merge, and process states from both data and control planes. This
paper sheds light upon the benefits of utilizing such an approach to
support feature extraction and data collection for network automation."
}