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:
We especially encourage submissions which include the public release of systems-related datasets for use by the wider research community.
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.
