Communication networks have evolved drastically in the last decade. Whereas networks largely used to provide dumb connectivity pipes interconnecting its end users, current network technology is tightly interconnected with the cloud, leading to a plethora of advanced services, a drastic increase in network usage, and strongly evolved data and control planes. Software-based functionality is now deeply changing the nature of both the control and data plane of our networking infrastructure through SDN and NFV technology respectively. This has introduced tremendous programmability and flexibility but also a range of uncertainties in the performance, security and management of our networks. Less functionality is now specified in standardized protocols or hardcoded in our data plane hardware.
Up to recently, machine learning techniques have been used in networking mostly for mechanisms outside of the control loop and outside of the fast path of our networks. Whereas machine learning has been effectively used for anomaly detection, network prediction or analytical purposes, the increasing network softwarization is now creating potential for apply recent evolutions in machine learning to optimize the actual control- as well as the data plane operation of networking infrastructure. Such applications might, for example, learn and optimize network performance relationships between softwarized network functions, presented loads and potential software/hardware configurations. The increasing availability of monitoring data and associated monitoring platforms might even further fuel advanced ML techniques including deep learning in exploiting ML-driven network approaches.
This workshop aims to bring together network researchers and machine learning experts with network operators and vendors to identify key opportunities, challenges and preliminary solutions for machine learning in softwarized networks. This includes the discussion of relevant data sets, best practices, and/or insights from earlier attempts. Particular topics of interest, but not limited to the following are:
- ML for performance analysis & optimization of (softwarized) networks
- ML for performance optimization of (softwarized) networks
- Security improvement through the use of Machine Learning
- Network Data Plane and HW acceleration techniques in support of ML
- ML for closed-loop network control (e.g., network resource allocation in a wireless network, computing MAC schedules or taking handover decisions)
- ML in Software-Defined Networking
- Network orchestration enhanced with ML (e.g., scaling, placement, admission control, load-dependent flow routing, …)
- Monitoring techniques in support of network ML (e.g., compressing monitoring data to meaningful results or deciding where in a network monitoring is required to detect inconsistent behavior)
This workshop is a satellite workshop of the NetSoft 2020 conference June 29-July 3, 2020, in Ghent, Belgium.
Interested authors are invited to submit papers of up to 8 pages (including references), presenting industrial innovations, work in progress, research results or technical developments.
Accepted and presented workshop papers will be published in the conference proceedings and will be submitted to IEEE Xplore. For more details about submission, please check the following websites:
- Submission link for NetLearn 2020: EDAS link
- Further submission information: https://netsoft2020.ieee-netsoft.org/authors/
Important: Please check NetSoft 2020 publication and no-show policy in the conference website at https://netsoft2020.ieee-netsoft.org/authors/publication-and-no-show-policy/.
- Paper submission: February 14th, 2020
- Notification of acceptance: March 23rd, 2020
- Camera-ready paper: April 6th, 2020
- Wouter Tavernier, Ghent University & imec, Belgium
- Holger Karl, Paderborn University, Germany
- Alex Galis, University College of London, United Kingdom
- Antonio Manzalini, Telecom Italia, Italy
- Arunselvan Ramaswamy, Paderborn University, Germany
- Ahmed M. Abdelmoniem, KAUST, Saudi Arabia
- Bin Cheng, NEC, Germany
- Chadi Barakat, INRIA, France
- Christian Rothenberg, University of Campinas, Brazil
- Feng Li, Shandong University, China
- Fulvio Risso, Politecnico di Torino, Italy
- Gianluca Reali, CNIT, Italy
- Mats Bengtsson, KTH, Sweden
- Olaf Landsiedl, Kiel University, Germany
- Oliver Hohlfeld, Brandenburg University of Technology, Germany
- Pan Hui, University of Helsinki, Finlan
- Philippe Owezarski, LAAS-CNRS, France
- Rebecca Steinert, RISE, Sweden
- Rolf Stadler, KTH, Sweden
- Stefan Schmid, University of Vienna, Austria
- Wolfgang John, Ericsson, Sweden