Categories |
NETWORKING
SDN
NFV
MACHINE LEARNING
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About |
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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. |
Call for Papers |
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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:
Paper Submission 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:
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/. Important Dates
Workshop Organization Chairs
Technical Program Committee
Workshop(s) on related topics
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Summary |
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NetLearn 2020 : Workshop on Machine Learning for Softwarized Networks (NetLearn) co-located with Netsoft 2020 will take place in Ghent, Belgium. It’s a 1 day event starting on Jun 29, 2020 (Monday) and will be winded up on Jun 29, 2020 (Monday). NetLearn 2020 falls under the following areas: NETWORKING, SDN, NFV, MACHINE LEARNING, etc. Submissions for this Workshop can be made by Feb 14, 2020. Authors can expect the result of submission by Mar 23, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Apr 06, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the NetLearn 2020
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Credits and Sources |
| [1] NetLearn 2020 : Workshop on Machine Learning for Softwarized Networks (NetLearn) co-located with Netsoft 2020 |
