SAC – 2nd International Workshop on System Analytics and Characterization
Co-located with IFIP WG 7.3 PERFORMANCE 2020
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.
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 ‘moonshot’’ ideas to stimulate discussions and new collaborations.
Topics of Interest
Topics of interest include, but are not limited to:
Submission deadline: September 1, 2020
Notification of acceptance: September 30, 2020
Workshop day: November 6, 2020
Submissions should take the form of a short paper, not to exceed 4 pages in length, in ACM format. Submissions will be double-blind. Please do not include identifying information in the submission. If you are building on your past work, please cite it as you would any other paper. For an accepted paper, at least one author must attend. Submissions that include public release of code and datasets will be given special consideration.
Accepted papers will be published in the workshop proceedings and 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 All submissions should be original, unpublished work.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.
Avani Wildani, Emory University, US
Ian Adams, Intel Research, US