2^nd International Workshop on Performance Analysis of Big data Systems (PABS)

OVERVIEW
Today, big data systems deal with volume, velocity, variety and veracity of the application data which may be deployed on dedicated single high performance systems (such as Netezza), dedicated commodity based clusters or shared architectures such as clouds. We witness an explosive growth in the complexity, diversity, number of deployments and capabilities of big data processing systems such as Map-Reduce, Hbase, Hive, Cassandra, Big Table, Hyracks, Dryad, Pregel and Mongo DB. The big data system may use new operating system designs, advanced data processing algorithms, parallelization of application, high performance computing architectures such as GPUs etc. and clusters to improve the performance. Looking at the volume of data to mine, and complex architectures, one may need to monitor, analyze, identify or predict bottlenecks to optimize the system and improve its performance.

The workshop on performance analysis of big data systems (PABS) aims at providing a platform for scientific researchers, academicians and practitioners to discuss techniques, models, benchmarks, tools and experiences while dealing with performance issues in big data systems. The primary objective is to discuss performance bottlenecks and improvements during big data analysis using different paradigms, architectures and technologies such as Map-Reduce, Hbase, MPP, Big Table, NOSQL, graph based models and any other new upcoming paradigms. We propose to use this platform as an opportunity to discuss systems, architectures, tools, and optimization algorithms that are parallel in nature and hence make use of advancements to improve the system performance. This workshop shall focus on the performance challenges imposed by big data systems and on the different state-of-the-art solutions proposed to overcome these challenges.

TOPICS
All novel performance analysis or prediction techniques, benchmarks, architectures, models and tools for data-intensive computing system for optimizing application performance on cutting-edge high performance solutions are of interest to the workshop. Examples of topics include but not limited to:

• Performance analysis and optimization of Big data systems and technologies.
• Deployment of Big Data technology/application on High performance computing architectures.
• Case studies/ Benchmarks to optimize/evaluate performance of Big data applications/systems and Big data workload characterizations.
• Tools or models to identify performance bottlenecks and /or predict performance metrics in Big data
• Performance analysis while querying, visualization and processing of large network datasets on clusters of multicore, many core processors, and accelerators.
• Performance issues in heterogeneous computing for Big data architectures.
• Analysis of Big data applications in science, engineering, finance, business, healthcare and telecommunication etc.
• Data structure and algorithms for performance optimizations in big data systems.

IMPORTANT DATES

• November 30 December 08 - Paper Submission deadline
• December 10 December 20 - Author Notification
• December 13 December 25 - Camera ready paper deadline
• March 12 - Workshop date

PROGRAM SCHEDULE

INVITED SPEAKERS
[ To be announced ]

SUBMISSIONS
Submissions describing original, unpublished recent results related to the workshop theme, upto 6 pages in standard ACM format can be submitted through the easychair conference system, following this link: EasyChair

In case of any difficulty please contact rekha.singhal@tcs.com or d.chahal@tcs.com. The submissions must be in pdf format.

Accepted technical papers will be included in the ACM Digital Library

PROGRAM CO-CHAIRS

• Rekha Singhal, Performance Engineering Research Centre, TCS Innovations Lab, India.
• Dheeraj Chahal, Performance Engineering Research Centre, TCS Innovations Lab, India.

TECHNICAL PROGRAM COMMITTEE

• Amitabha Bagchi , IITD, India
• Amy Apon, Clemson University, USA
• Arno Jacobsen, University of Toronto, Canada
• Bojan Cukic, UNC, USA
• Dhableshwar Panda, Ohio State University, USA
• Gautam Shroff, TCS Innovation Lab, India
• Kishor Trivedi, Duke University
• Jeff Ullman, Stanford University and Gradiance, USA
• Rajesh Mansharamani, CMG India
• Saumil Merchant, Shell, India
• Sebastien Goasguen, Citrix, Switzerland
• Steven J Stuart, Clemson University, USA
• Veena Mendiratta, Alcatel-Lucent, USA
• Vikram Narayana, George Washington University, USA
• Zia Saquib, CDAC, India