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DTSTAMP:20210402T160546Z
LOCATION:Track 9
DTSTART;TZID=America/New_York:20201113T143000
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UID:submissions.supercomputing.org_SC20_sess231@linklings.com
SUMMARY:2nd Workshop on Machine Learning for Computing Systems
DESCRIPTION:Workshop\n\nScheduling in Data Centers Running on Renewable En
 ergy with Deep Reinforcement Learning\n\nVenkataswamy, Grimshaw\n\nMost da
 tacenters operate on electricity generated using non-renewable sources. El
 ectricity cost is a significant part of the operational expense in datacen
 ters. Higher electricity costs translate to endusers paying higher prices 
 for cloud services or service providers seeing reduced profits. To redu...
 \n\n---------------------\nExplainable Machine Learning Frameworks for Man
 aging HPC Systems\n\nAksar, Ateş, Leung, Coskun\n\nRecent research on
  supercomputing proposes a variety of machine learning frameworks that are
  able to detect performance variations, find optimum application configura
 tions, perform intelligent scheduling or node allocation and improve syste
 m security. Although these goals align well with HPC systems...\n\n-------
 --------------\nInvestigating the Efficacy of Unstructured Text Analysis f
 or Node Failure Detection in Syslog\n\nFelkner, Moore\n\nEach node of a su
 percomputer produces a detailed log of its operation, called a syslog. It 
 is impossible for system administrators to review all syslog data produced
  by the thousands of compute nodes associated with a single HPC machine. A
 nalysis of these logs to detect and predict failures, however...\n\n------
 ---------------\nEarly Prediction of High-Performance Computing Job Outcom
 es via Modeling System Text Logs\n\nDeLucia, Moore\n\nThe massive scale of
  high-performance computing (HPC) machines necessitates using automatic st
 atistical methods to assist human operators in monitoring day-to-day behav
 ior. We address the problem of identifying problematic compute jobs by mod
 eling system logs, which record all activities on the mach...\n\n---------
 ------------\nA Year of Automated Anomaly Detection in a Datacenter\n\nAhm
 ed, Porter, Abdelmutalab, Ricci\n\nAnomaly detection based on machine lear
 ning can be a powerful tool for understanding the behavior of large, compl
 ex computer systems in the wild. The set of anomalies seen, however, can c
 hange over time: as the system evolves, is put to different uses and encou
 nters different workloads, both its typ...\n\n---------------------\nMLCS 
 – Introduction: 2nd Workshop on Machine Learning for Computing Systems\n\n
 Moore, Konate, Gamblin, Jacobs\n\nThe 2nd Workshop on Machine Learning for
  Computing Systems (MLCS) brings together researchers across machine learn
 ing, data science, HPC and computing systems to discuss the use, developme
 nt and rigorous evaluation of data-driven statistical techniques for the m
 anagement, design and optimization of ...\n\n---------------------\nMLCS –
  Keynote\n\n\n\n---------------------\nMLCS – Panel\n\n\n\n---------------
 ------\nMLCS – Break\n\n\n\n\nRegistration Category: Workshop Reg Pass
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