MLCS20: 2nd Workshop on Machine Learning for Computing Systems, at SIAM Data Mining 2020 co-located with SIAM Data Mining 2020 Cincinnati, OH, United States, May 7-10, 2020 |
Conference website | https://mlcsworkshop.weebly.com/ |
Submission link | https://easychair.org/conferences/?conf=mlcs20 |
2nd Machine Learning for Computing Systems Workshop at SIAM Data Mining 2020
Recently, there is a rising interest in the use of machine learning techniques to better understand, analyze, manage, and design large-scale computing facilities. Interdisciplinary research at the intersection of machine learning, data science, and systems has already produced advances in memory error mitigation, datacenter cooling, system log analysis, job scheduling, and database indexing, among others. Especially as the machine learning community builds a focus on human-understandable models, learned models become extremely attractive for systems/high performance comuting (HPC)-related decision support and development of data-driven tools to assist of human experts. Additionally, it is frequently the case that HPC-related problems are also related to open machine learning research areas, such as anomaly detection within near-natural language text (e.g. system logs, console logs, etc.), and there is a definite need for collaboration between HPC domain experts and statistical modeling / machine learning experts.
While there is a rise in ML-for-Systems workshops, the community seems to be fragmented based on their background in industry, academia, or national laboratories. The audience we target through MLCS ‘20 is intentionally broad and inclusive, ranging from seasoned machine learning and systems experts through students new to the field, and spanning across industry, academia, and government. While we aim to be a conduit for productive conversations between professional experts who may not otherwise connect, we will also welcome and encourage students and newcomers who may not have previously considered our interdisciplinary field. We will accomplish this by explicitly soliciting not only fully baked research results, but also works-in-progress, extended abstracts, and position papers.
For more information, see the Machine Learning for Computing Systems website.
Important Dates
Submission deadline: March 8, 2020
Author notification: March 22, 2020
Camera-ready deadline: April 1, 2020
Submission Guidelines
We solicit 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.
- Submitted full papers must be no longer than 8 single-spaced 8.5”x11” pages, including figures, tables, and references; in the SIAM format (two-column format, using 10-point type on 12-point (single-spaced) leading; and a text block 6.5” wide x 9” deep). Author names and affiliations should appear on the front page.
- Submitted short work-in-progress, experience, or position papers must be no longer than 4 single-spaced 8.5” x 11” pages, including figures, tables, and references; in the SIAM format (two-column format, using 10-point type on 12-point (single-spaced) leading; and a text block 6.5” wide x 9” deep). Author names and affiliations should appear on the front page. Work-in-progress, experience, and position papers will be considered for either an oral presentation or a poster presentation, based on number of submissions.
- Submitted extended abstracts must be no longer than 2 single-spaced 8.5” x 11” pages, including figures, tables, and references; in the SIAM format (two-column format, using 10-point type on 12-point (single-spaced) leading; and a text block 6.5” wide x 9” deep). Author names and affiliations should appear on the front page. Extended abstracts will be considered for either an oral presentation or a poster presentation, based on number of submissions.
Submitted contributions should present original theoretical and/or experimental research in any of the areas in any of the areas listed above that has not been previously published, accepted for publication, s not currently under review by another conference of journal, or makes significant progress beyond a previously-published version.
Submitted contributions will be peer-reviewed by multiple program committee members, and acceptance decisions will be based on novelty, technical soundness, and relevance to workshop themes.
Accepted contributions will not be formally published, but will be archived on the workshop website.
List of Topics
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.
Venue
MLCS 20 will be held at SIAM Data Mining 2020, in Cincinnati, Ohio, USA.
Contact
Questions regarding this CFP or the MLCS workshop should be directed to mlcs20 [at] easychair [dot] org