Call for Papers: SLAML 2011

_Managing Large-Scale Systems via the Analysis of System Logs and the 
Application of Machine Learning Techniques (SLAML 2011)_

At the ACM Symposium on Operating Systems Principles

October 23-26, 2011

Carcais, Portugal

  Important Dates

    *

      Full paper submission due:* Friday, June 17*^*th* *, 2011*

    *

      Notification of acceptance: *Friday, July 15*^*th* *, 2011*

    *

      Final papers due: *Friday, August 12*^*th* *, 2011*

  Overview

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 of 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 also sometimes 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

Topics include but are not limited to:

    *

      Reports on publicly available sources of sample system logs

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      Prediction of malfunction or misuse based on system data

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      Statistical analysis of system logs

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      Applications of Natural-Language Processing (NLP) to system data

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      Techniques for system log analysis, comparison, standardization,
      compression, anonymization, and visualization

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      Applications of log analysis to system administration problems

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      Use of machine learning techniques to address reliability,
      performance, power management, security, fault diagnosis,
      scheduling, or manageability issu

    *

      Challenges of scale in applying machine learning to large systems

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      Integration of machine learning into real-world systems and processes

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      Evaluating the quality of learned models, including assessing the
      confidence/reliability of models and comparisons between different
      methods

  Workshop Organizers

*Program Co-Chairs*

    *

      Peter Bodik, /Microsoft Research/

    *

      Marc Casas, /Lawrence Livermore National Laboratory/

    *

      Greg Bronevetsky, /Lawrence Livermore National Laboratory/

  Submission Guidelines

Submitted papers must be no longer than 8 (8) 8.5"x11" or A4 pages, 
using a 10 point font on 12 point (single spaced) leading, with a 
maximum text block of 6.5 inches wide by 9 inches deep. The page limit 
includes everything except for references, for which there is no limit. 
The use of color is acceptable, but the paper should be easily readable 
if viewed or printed in gray scale. Authors must make a good faith 
effort to anonymize their submissions, and they should not identify 
themselves either explicitly or by implication (e.g., through the 
references or acknowledgments). Submissions violating the detailed 
formatting and anonymization rules on the Web site will not be 
considered for publication. There will be no extensions for reformatting.

Blind reviewing of full papers will be done by the program committee, 
with limited use of outside referees. Papers will be provisionally 
accepted subject to revision and approval by a program committee member 
acting as a shepherd. On acceptance, authors will be required to sign an 
ACM copyright release form. Your submission indicates that you agree to 
this. Papers will be held in full confidence during the reviewing 
process, but papers accompanied by nondisclosure agreement forms are not 
acceptable and will be rejected without review. Authors of accepted 
papers will be expected to supply electronic versions of their papers 
and encouraged to supply source code and raw data to help others 
replicate and better understand their results.