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DTSTAMP:20210402T160558Z
LOCATION:Track 4
DTSTART;TZID=America/New_York:20201112T164500
DTEND;TZID=America/New_York:20201112T171500
UID:submissions.supercomputing.org_SC20_sess210_ws_indisc108@linklings.com
SUMMARY:AI for Networking: the Engineering Perspective
DESCRIPTION:SCinet, Workshop\n\nAI for Networking: the Engineering Perspec
 tive\n\nKiran\n\nThis talk explores machine learning, deep learning and AI
  techniques when applied to operational networking and distributed computi
 ng problems.  With advances in computing, data is being produced at expone
 ntial rates requiring highly flexible mobility across HPC computing and di
 stributed facilities. Networks are the essential bloodline to science coll
 aborations across the globe such as in high-energy physics, earth sciences
  and genomics. However, upgrading network hardware, with high-end routers 
 and optic fibers, to cope with this data revolution can cost millions of d
 ollars. We are exploring artificial intelligence to design and efficiently
  manage distributed network architectures to improve data transfers or rou
 ting, guarantee high-throughput and improve traffic engineering. I will di
 scuss some of the challenges we face when deploying AI on the network.\n\n
 Tag: Big Data, Data Analytics, Compression, and Management, Datacenter, Ne
 tworks, Performance/Productivity Measurement and Evaluation, SCinet, Softw
 are-defined networking\n\nRegistration Category: Workshop Reg Pass
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