[Storage-research-list] System Analytics and Characterization (SAC 2016)

[Mesnier, Michael]

SAC is a new workshop focused on systems and techniques for evaluating big data.



It will co-located with SIGMETRICS this year.



Below is the call for papers. Please consider submitting your work!



https://sites.google.com/site/sacconference2016



Mike Mesnier

Intel Labs



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1st International Workshop on System Analytics and Characterization (SAC 2016)



Co-located with SIGMETRICS 2016



The statistical techniques at the forefront of the big data movement are uniquely suited to the systems community; even modest sized systems can easily produce hundreds of millions of data entries per hour. Efficiently tracking and mining this data has the potential for significant benefits, ranging from performance optimization in data centers to fundamental architectural changes in how we design and organize scalable systems.  To apply these techniques successfully, as well as to understand the new challenges in data-driven systems design and administration, we must take a close look at current best practices and explore novel techniques in trace collection, validation, and analysis.







Important Dates:

Submission Deadline: April 1st, 2016, Anywhere-on-Earth (https://www.timeanddate.com/time/zones/aoe)

Author Notification: May 2nd, 2016

Workshop Date: June 18th, 2016

Venue: SAC 2016 is co-located with SIGMETRICS in scenic Antibes Juan-Les-Pins, France: http://www.antibesjuanlespins-congres.com/



Scope:





Submissions on applications, results and experiences are of course welcome, but we have a particular interest in submissions with novel applications, new unsolved problems, and 'pie-in-the-sky' ideas to stimulate discussions and new collaborations.  Within this scope, topics of interest include, but are not limited to:





*Efficient Data Gathering: Possible topics include low-overhead workload collection, sub-sampling, and synthetic workload generation.

*Trace Validation and Replay: Possible topics include instrumentation, trace reconstruction, and hint generation.

*Characterization and Prediction: Possible topics include novel applications of data mining to system traces and performance analysis, predicting and analysis in real time systems, and workload characterization.





*Submission Guidelines:



Submissions should take the form of an extended abstract, not to exceed 3 pages in length, in ACM format. For an accepted paper, at least one author must attend. Accepted abstracts will be given a 10-15 minute time slot for presentation, with at least 5 minutes for questions and discussion after each presentation. Come prepared!





Submissions under NDA will not be considered. All submissions should be original, unpublished work.





Papers will be submitted through Easychair, which will open 2 weeks prior to the submission deadline.





Committee:

General Chairs

Avani Wildani (Emory University, USA)

Ian Adams (Intel Research, USA)



TPC:

Cameron Walker (U Auckland)

David L Hart (NCAR)

Bill Anderson (NCAR)

Tipp Moseley (Google)

Mike Mesnier (Intel)

Kirk Cameron (Virginia Tech)

Hanan Lutfiyya (U Waterloo)

Dirk Meister (Pure Storage)

Jerzy Proficz (Gdańsk University of Technology) Alma Riska (NetApp ATG) Vasily Tarasov (IBM Almaden) Mary Baker (HP) Geoff Kuenning (Harvey Mudd)
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