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DTSTART;TZID=America/Chicago:20181112T105000
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UID:submissions.supercomputing.org_SC18_sess169_ws_daac111@linklings.com
SUMMARY:Tivan: A Scalable Data Collection and Analytics Cluster
DESCRIPTION:Hugh Greenberg and Nathan Debardeleben (Los Alamos National La
 boratory)\n\nLog analysis is a critical part of every data center to diagn
 ose system issues and determine performance problems. As data centers incr
 ease in size, the need for a dedicated, scalable log analysis system becom
 es necessary. At Los Alamos National Laboratory, our clusters and support 
 infrastructure are currently producing over seventy million logs a day. Th
 e large amount of log data makes it extremely difficult for system adminis
 trators, application developers, and researchers to find relevant events a
 nd perform detailed analysis. In order to manage this data and to run scal
 able analytics, we designed and deployed a data analytics cluster based on
  open source software. In this paper, we describe the design and trade-off
 s, the current uses of this system, and analytics utilizing the system tha
 t are aiding in the discovery of system issues. We have found that this da
 ta analytics cluster has helped us to proactively identify cluster issues 
 more efficiently.\n\nTag: Build Systems, Clouds and Distributed Computing,
  Data Analytics, Deep Learning, Education, HPC Center Planning and Operati
 ons, Heterogeneous Systems, Resource Management, Scheduling, Scientific Co
 mputing, State of the Practice, Visualization, HPC, Containers, Datacenter
 , Industry, monitoring\n\nRegistration Category: Workshop Reg Pass\n\nSess
 ion Chairs: Yong Chen (Texas Tech University), Tim Cockerill (Texas Advanc
 ed Computing Center (TACC)), Dong Dai (University of Delaware), and Alan S
 ill (Texas Tech University)\n\n
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