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BEGIN:VEVENT
DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost127@linklings.com
SUMMARY:Machine Learning for Data Transfer Anomaly Detection
DESCRIPTION:Posters, Research Posters\n\nMachine Learning for Data Transfe
 r Anomaly Detection\n\nBhuiyan, Cooper, Arslan\n\nData transfer performanc
 e is critical for many science applications that rely on remote clusters t
 o process the data. Despite the presence of high-speed research networks w
 ith up to 100 Gbps speeds, most data transfers obtain only a fraction of n
 etwork bandwidth, due to a variety of reasons. This project aims to pinpoi
 nt the underlying causes for performance anomalies by collecting and proce
 ssing real-time performance metrics from file systems, data transfer nodes
  and networks such that proper actions can be taken for timely mitigation 
 of the issues. As veracity and velocity of performance statistics are beyo
 nd what human operators can handle, we trained a neural network (NN) model
  to analyze the data in real-time and make high-accuracy predictions. The 
 results indicate that NN can find the correct anomaly type with 93% accura
 cy.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
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