MLCS 2020
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2nd workshop on
​Machine Learning for Computing Systems

Friday, November 13, 2020; 2:30pm-6:00pm EST
Virtual
Hosted at SC '20

Topics of interest

We are soliciting full papers, short work-in-progress papers, extended abstracts, experience papers, and position papers on the broad theme of data-driven statistical modeling of large-scale computing systems, including but not limited to:

Use of machine learning or data science in the context of better understanding any of the following large-scale computing system issues:
  • Hardware faults and errors
  • Software errors
  • Telemetry data (temperature, voltages, cooling apparatus)
  • Power consumption
  • Facilities / building control
  • Job scheduling
  • Filesystem logs
  • Network logs
  • Syslog or console logs
  • Error detection and correction
  • Resilience and fault tolerance
  • Failure troubleshooting / assistance of human experts
  • Assistance of non-expert users
  • System security
  •  Use of explainable machine learning models for systems-related decision support
    • Including user/human-subject studies
  • Modeling techniques incorporating human expert knowledge along with knowledge extracted from data:
    • Use of these models to evaluate, confirm, or refute human assumptions
  • New or improved machine learning models particularly suited for computing system problems
  • Tools, at any stage of development, using data-driven technologies for some aspect of systems monitoring or design
  • Experience reports detailing successes and failures of machine learning applied to systems
  • Formulations of unsolved data-related systems problems with the potential for machine learning

We especially encourage submissions which include the public release of systems-related datasets for use by the wider research community.
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  • Home
  • Topics
  • Submission
  • Program
  • Committee
  • Contact
  • Previous Editions: 2018