CALL FOR PAPERS

The IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2017)

http://userpages.umbc.edu/~jianwu/BPOD/BPOD-2017.html
one day during December 11-14, 2017, Boston, MA, USA
at the IEEE Big Data 2017 Conference (IEEE BigData 2017)

The program and presentations of the workshop can be found at http://userpages.umbc.edu/~jianwu/BPOD/BPOD-2017-Papers.html.

Description

Users of big data are often not computer scientists. On the other hand, it is nontrivial for even experts to optimize performance of big data applications because there are so many decisions to make. For example, users have to first choose from many different big data systems such as those dealing with structured data (e.g., Apache Hbase, Mongo DB, Apache Hive, Apache Accumulo, Presto, Spark SQL), graph data (e.g., Pregel, Giraph, GraphX, GraphLab), and streaming data (e.g., Apache Storm, Apache Heron, Apache Flink, Samza). In addition, there are numerous parameters to tune to optimize performance of a specific system. To make things more complex, users may worry about not only response time or throughput, but also quality of results, monetary cost, security and privacy, and energy efficiency. In more traditional relational databases these complexities are handled by query optimizer and other automatic tuning tools (e.g., index selection tools) and there are benchmarks to compare performance of different products. Such tools are not available for big data environment and the problem is probably more complicated than the problem for traditional relational databases.

The aim of this workshop is to bring researchers and practitioners together to better understand the problems of optimization and performance tuning in a big data environment, to propose new approaches to address such problems, and to develop related benchmarks, tools and best practices.

Topics of interests include, but are not limited to:

• Theoretical and empirical performance model for big data applications
• Benchmark and comparative studies for big data processing and analytic platforms
• Monitoring, analysis, and visualization of performance in big data environment
• Workflow/process management & optimization in big data environment
• Performance tuning and optimization for specific big data platforms or applications (e.g., No-SQL databases, graph processing systems, stream systems, SQL-on-Hadoop databases)
• Performance tuning and optimization for specific data sets (e.g., scientific data, spatio data, temporal data, text data, images, videos, mixed datasets)
• Case studies and best practices for performance tuning for big data
• Cost model and performance prediction in big data environment
• Impact of security/privacy settings on performance of big data systems
• Self adaptive or automatic tuning tools for big data applications
• Big data application optimization on High Performance Computing (HPC) and Cloud environments

Important Dates

• Paper Submission: Oct 23, 2017 (Extended)
• Decision Notification: Nov 13, 2017
• Camera-Ready Copy Due Date: Nov 20, 2017

Paper Submission (To be updated)

Authors are invited to submit full papers (maximal 10 pages) or short papers (maximal 6 pages) as per IEEE 8.5 x 11 manuscript guidelines (download Word templates, download PDF templates or LaTeX templates). All papers must be submitted via the conference submission system for the workshop.

At least one author of each accepted paper is required to attend the workshop and present the paper. All the accepted papers by the workshops will be included in the Proceedings of the IEEE Big Data 2017 Conference (IEEE BigData 2017) which will be published by IEEE Computer Society.

Workshop Chairs

• Zhiyuan Chen, University of Maryland, Baltimore County, U.S.A, zhchen-AT-umbc.edu
• Jianwu Wang, University of Maryland, Baltimore County, U.S.A, jianwu-AT-umbc.edu

Program Committee

• Ilkay Altintas, University of California San Diego
• David Bermbach, TU Berlin
• Chritian Konig, Microsoft Research
• Shiyong Lu, Wayne State University
• Frank Pallas, TU Berlin
• Madhusudhan Govindaraju, Binghamton University
• Min Li, IBM TJ Waston Research Center
• Xiaoyi Lu, Ohio State University
• Chen Liu, North China University of Technology
• Yue Hu, Louisiana State University
• Wanghu Chen, Northwest Normal University, China

Steering Committee

• Geoffrey Fox, Indiana University
• Le Gruenwald, University of Oklahoma
• Dhabaleswar K. (DK) Panda, Ohio State University
• Jianfeng Zhan, Chinese Academy of Sciences

Back to Jianwu Wang's homepage.

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