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UID:349@lincs.fr
DTSTART;TZID=Europe/Paris:20171122T140000
DTEND;TZID=Europe/Paris:20171122T150000
DTSTAMP:20180115T144323Z
URL:https://www.lincs.fr/events/optimal-flow-segmentation-in-sdn-with-memo
 ry-constraints-a-reinforcement-learning-approach/
SUMMARY:Optimal flow segmentation in SDN with memory constraints: a
 reinforcement learning approach
DESCRIPTION:Datacenter flow routing relies on packet-in messages generated
 by switches and directed to the controller upon new flows arrivals. The SDN
 controller reacts to packet-in events by installing forwarding rules in the
 memory of all switches along an optimized path. Since flow arrival rates
 can peak to millions per second\, a relevant constraint is represented by
 the scarce amount of TCAM memory on switches. We assume that if a routing
 table is full\, a flow will be routed on a default\, sub-optimal path. A
 viable solution is to restrict the optimized traffic to critical flows:
 this corresponds to performing traffic segmentation\, prioritizing larger
 flows over smaller flows. However\, choosing the optimal threshold to
 discriminate optimized flows from non-optimized flows is not a trivial
 task. This work focuses on learning the optimal flow segmentation policy
 under memory constraints. We formulate this task as a Markov decision
 problem. Based on the structure of the optimal stationary policy\, we
 propose a reinforcement learning algorithm tailored to the problem at hand.
 We prove it is adaptive\, correct and has a polynomial time complexity.
 Finally\, numerical experiments characterize the performance of the
 algorithm.Joint work with Francesco de Pellegrini (Fondazione Bruno
 Kessler)\, Lorenzo Maggi (Huawei Algorithmic and Mathematical Sciences
 Lab\, Paris)
CATEGORIES:Seminars,Youtube
LOCATION:LINCS Seminars room\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.828400;2.356897
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 room:geo:48.828400,2.356897
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DTSTART:20171029T020000
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TZOFFSETTO:+0100
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