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BEGIN:VEVENT
UID:558@lincs.fr
DTSTART;TZID=Europe/Paris:20200923T140000
DTEND;TZID=Europe/Paris:20200923T150000
DTSTAMP:20200925T133657Z
URL:https://www.lincs.fr/events/seminar-presentation-by-a-new-lincs-associ
 ate-member-on-the-inference-of-high-speed-networks-behavior-via-machine-le
 arning/
SUMMARY:Seminar Presentation by a new LINCS associate member "On the
 inference of high-speed networks’ behavior via Machine Learning"
DESCRIPTION:Network Function Virtualization (NFV) is a novel trend in IT
 networks which advocates replacing hardware middleboxes with equivalent
 pieces of code executed on general-purpose servers.\nThis on-going process
 of network softwarization can help network operators to reduce the
 operational and management costs of their network
 infrastructures.\nAlongside the benefits from the operator's perspective\,
 new strategies to provide the network's resources to users are
 arising.\nFollowing the principle of "everything as a service"\, multiple
 tenants can access the required resources -- typically CPUs\, NICs\, or RAM
 -- according to a Service-Level Agreement (SLA). This provides the
 opportunity for network managers to continuously monitor the network's
 behavior\, collect a huge amount of network data that can be used\, in
 conjunction with machine learning algorithms\, to solve several problems
 (from resource allocation\, to intrusion
 detection/prevention).\nUnfortunately\, there is no free lunch\, and
 current machine learning techniques require a huge amount of computational
 resources: in the case of softwarized networks\, allocating the
 computational resources requires a tradeoff between the network
 communication's capabilities (i.e.\, the "speed") and the quality of the ML
 algorithm (i.e.\, the "intelligence").\nIn this talk\, I will present a
 network inference problem\, which consists in identifying the global
 behavior of a high-level network function executed on top of a commodity
 server\, by accessing low-level information represented by the server's CPU
 utilization.\nThe focus of this presentation will be on the performance
 (accuracy) of the ML algorithm utilized to make the prediction\, and on the
 impact of such an algorithm on the overall network performance.\nI will
 finally identify the most promising approaches to be able to deploy machine
 learning algorithms together with real-world high-speed network
 applications.
CATEGORIES:Machine Learning,Seminars,Youtube
LOCATION:Paris-Rennes Room (EIT Digital)\, 23 avenue d'Italie\, 75013
 Paris\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=23 avenue d'Italie\, 75013
 Paris\, France;X-APPLE-RADIUS=100;X-TITLE=Paris-Rennes Room (EIT
 Digital):geo:0,0
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TZID:Europe/Paris
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DTSTART:20200329T030000
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