Workshop on Managing Systems via Log Analysis and Machine
Learning Techniques (SLAML '10)
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October 2-3, 2010
Vancouver, BC, Canada
(at OSDI)
http://www.usenix.org/events/slaml10/cfp/
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******** DEADLINE EXTENDED ***********
Now accepting full both 8-page full papers and 3-page position papers!
FULL PAPER SUBMISSION: Sunday, July 11, 2010, 11:59 p.m. PDT
AUTHOR NOTIFICATION: Friday, August 20, 2010
FINAL PAPERS DUE: Thursday, September 16, 2010
SLAML '10 combines the Workshop on the Analysis of System Logs (WASL) and
the Workshop on Tackling Computer Systems Problems with Machine Learning
Techniques (SysML). We welcome contributions related to either of these
important and related topics.
Modern large-scale systems are challenging to manage. Fortunately, as these
systems generate massive amounts of performance and diagnostic data, there is
an opportunity to make system administration and development simpler via
automated techniques to extract actionable information from the data. This
workshop addresses this problem in two thrusts: (i) the analysis of raw system
data logs and (ii) the application of machine learning to systems problems. We
expect the large overlap in these topics to promote a rich interchange of ideas
between the areas.
Log Analysis:
It is well known that raw system logs are an abundant source of information
for the analysis and diagnosis of system problems and prediction of future
system events. However, a lack of organization and semantic consistency between
system data from various software and hardware vendors means that most of this
information content is wasted. Current approaches to extracting information
from the raw system data capture only a fraction of the information available
and do not scale to the large systems common in business and supercomputing
environments. It is thus a significant research challenge to determine how to
better process and combine information from these data sources.
Machine Learning:
The large scale of available data requires automated and machine-assisted
analysis. Statistical machine learning techniques have recently shown great
promise in meeting the challenges of scale and complexity in datacenter-scale
and Internet-scale computing systems. However, applying these techniques to
real systems scenarios requires careful analysis and engineering of the
techniques to fit them to specific scenarios; there is sometimes also the
opportunity to develop new algorithms specific to systems scenarios. This
workshop thrust thus also presents a substantial research area: the exploration
of new approaches to using machine learning to help us understand, measure, and
diagnose complex systems.
Topics include but are not limited to:
o Reports on publicly available sources of sample system logs
o Prediction of malfunction or misuse based on system data
o Statistical analysis of system logs
o Applications of Natural-Language Processing (NLP) to system data
o Techniques for system log analysis, comparison, standardization,
compression, anonymization, and visualization
o Applications of log analysis to system administration problems
o Use of machine learning techniques to address reliability, performance,
power management, security, fault diagnosis, scheduling, or manageability issues
o Challenges of scale in applying machine learning to large systems
o Integration of machine learning into real-world systems and processes
o Evaluating the quality of learned models, including assessing the
confidence/reliability of models and comparisons between different methods
SLAML '10 will be a 1.5-day workshop held immediately preceding the 9th
USENIX Symposium on Operating Systems Design and Implementation (OSDI '10),
which will take place October 4-6, 2010. SLAML'10 will begin on the afternoon
of October 2, 2010, and run through October 3, 2010.
Workshop Organizers:
Greg Bronevetsky, Lawrence Livermore National Laboratory
Kathryn Mohror, Lawrence Livermore National Laboratory
Alice Zheng, Microsoft Research
Submission Instructions:
Interested speakers should submit their full papers by June 13, 2010, via
the Web submission form, which will be available here soon. All papers will be
subject to peer review under conference standards. Authors may choose to submit
a paper anonymously or with author names visible to reviewers. Experience
reports and papers on work in progress are welcome as long as there is a clear
contribution. Paper submissions may be accepted as papers for the regular
workshop program or as posters for the poster session. Submissions must be in
PDF format and must be no longer than eight 8.5" x 11" pages, including figures
and tables, but not including references, formatted in two columns, using 10
point type on 12 point (single-spaced) leading, with the text block being no
more than 6.5" wide by 9" deep.
All papers will be available online to registered attendees before the
workshop. If your accepted paper should not be published prior to the event,
please notify [email protected]. The papers will be available online to
everyone beginning on the first day of the workshop, October 2, 2010.
Papers accompanied by nondisclosure agreement forms will not be considered.
Accepted submissions will be treated as confidential prior to publication on
the USENIX SLAML '10 Web site; rejected submissions will be permanently treated
as confidential.
Simultaneous submission of the same work to multiple venues, submission of
previously published work, or plagiarism constitutes dishonesty or fraud.
USENIX, like other scientific and technical conferences and journals, prohibits
these practices and may take action against authors who have committed them.
See the USENIX Conference Submissions Policy for details.
Questions? Contact your program co-chairs, [email protected], or the
USENIX office, [email protected].
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