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UID:submissions.supercomputing.org_SC21_sess346_ws_indis106@linklings.com
SUMMARY:NetGraf: An End-to-End Learning Network Monitoring Service
DESCRIPTION:Workshop\n\nNetGraf: An End-to-End Learning Network Monitoring
  Service\n\nMohammed, Kiran, Enders\n\nNetwork monitoring services are of 
 enormous importance to ensure optimal performance is being delivered and h
 elp determine any failing services. Particularly for large data transfers,
  checking key performance indicators like throughput, packet loss, and lat
 ency can make or break experiment results. However, network monitoring too
 ls are very diverse in metrics collected and dependent on the devices inst
 alled. Additionally, there are limited tools that can learn and determine 
 the cause of degraded performance. This paper presents NetGraf, a novel en
 d-to-end learning monitoring system that utilizes current monitoring tools
 , merges multiple data sources into one dashboard for easy use, and provid
 es machine learning libraries to analyze the data and perform real-time an
 omaly finding. Using a database backend, NetGraf can learn performance tre
 nds and show users if network performance has degraded. We demonstrate how
  NetGraf can easily be deployed through automation services and linked to 
 multiple monitoring sources to collect data. Via the machine learning inno
 vation and merging various data sources, NetGraf aims to fulfill the need 
 for holistic learning network telemetry monitoring. To the best of our kno
 wledge, this is the first-ever end-to-end learning monitoring service. We 
 demonstrate its use on two network setups to showcase its impact.\n\nTag: 
 Big Data, Cloud and Distributed Computing, Data Analytics, Data Management
 , Datacenter, Networks, Performance, Quantum Computing\n\nRegistration Cat
 egory: Workshop Reg Pass
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