[Idnet] Calll for participation - "Learning Methods for Control of Communication Networks"
Trimponias Georgios <firstname.lastname@example.org> Thu, 27 April 2017 02:47 UTC
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From: Trimponias Georgios <firstname.lastname@example.org>
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Thread-Topic: Calll for participation - "Learning Methods for Control of Communication Networks"
Date: Thu, 27 Apr 2017 02:46:59 +0000
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Subject: [Idnet] Calll for participation - "Learning Methods for Control of Communication Networks"
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Call for Participation Learning Methods for Control of Communications Networks RLDM Satellite Meeting June 14-15 2017 University of Michigan, Ann Arbor Learning methods have been successfully applied to various control problems in communications networks for more than four decades. Nevertheless, there has yet to be a concerted effort to systematically explore the potential performance benefits to be reaped by using learning methods in this domain. Given the continued growth in the size and dynamics of communications networks, in the number and location of communicating devices, and in the volume of traffic to be transported and the types of applications to be supported, the algorithms for controlling the behavior of a network should scale accordingly yet do so under uncertainty about the current state of the entire network. Learning methods hold promise for enabling large dynamic communications networks to effectively, efficiently, and autonomously accommodate increasing and varied user demand. Communications networks also offer in return a rich experimental domain for research on learning and decision making. The goal of this meeting is to foster collaboration between the communications networks and learning communities, bringing to bear powerful learning algorithms for control of communications networks and exposing a complex domain for research on learning methods. We welcome submissions of original research describing theoretical or empirical results using learning methods for network control. Here, the term 'network control' encompasses decision making at all time scales, ranging from processing individual packets and flows to network planning and design. Learning methods that require neither a detailed model of the network nor supervisory input to make appropriate decisions are of particular interest for this meeting. To participate in the meeting, you must prepare an extended abstract of at most four pages, inclusive of figures and references, and must submit the abstract directly to the organizers by 12 May 2017. Abstracts will be used to determine the speakers and the discussion topics for the meeting. Each participant's abstract will be made available electronically as part of the record of the meeting, provided the participant explicitly grants permission to do so. Abstract formatting: LaTex template: rldmsubmit.sty LaTex example: rldm.tex Abstract samples: rldm.pdf, rldm.rtf Organizers: Martha Steenstrup, Stow Research L.L.C. George Trimponias, Huawei Technologies Co., Ltd.
- [Idnet] Calll for participation - "Learning Metho… Trimponias Georgios