[hpc-announce] PABS CFP

[D Chahal]

                                        Call for papers: PABS 2018
                                                        (
https://web.rniapps.net/pabs/)
4th International Workshop on Performance Analysis of Big data Systems 
(PABS)
April 9, 2018
Berlin, Germany
to be held in conjunction with
International Conference on Performance Engineering (ICPE)-2018

Scope of the workshop:
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, case 
studies  and  experiences  while  dealing with performance issues in 
traditional and big data systems. The primary objective is to discuss 
performance bottlenecks and improvements during big data analysis using 
different paradigms, architectures and big data technologies. 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.  The accepted papers shall be published in ACM proceedings and 
digital library.

Topics
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.
·       Big data analytics using machine learning
·       In-memory analysis of big data
·       Performance Assured migration of traditional systems to Big data 
platforms
·       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.
·       Data intensive computing
·       Tools for big data analytics and management

IMPORTANT DATES 
·       January 15, 2018 - Submissions due
·       February 12, 2018 - Notification of acceptance
·       February 18, 2018 - Camera-ready copies due
·       April 09, 2018 - Workshop date
Submission details:
Submissions describing original, unpublished recent results related to the 
workshop theme, up to 6 pages
in ACM conference format can be submitted through EasyChair paper 
submission website  https://easychair.org/conferences/?conf=pabs2018.. 
All submission will be accepted in pdf format only. While the preferred 
mode of submission is through the paper submission website but in case of 
difficulty in online submission, authors may also submit their manuscripts 
over email to the workshop co-chairs.

Program Co-Chairs:
·       Rekha Singhal, TCS Research, India
rekha dot singhal at tcs.com
·       Dheeraj Chahal, TCS Research, India
d dot chahal at tcs.com

Technical Program committee:
·       Amy Apon, Clemson University, USA
·       Arindam Pal, TCS Research, India
·       Evgenia Smirni, College of Willian and Mary, USA
·       Giuliano Casale, Imperial College London, UK
·       Nicolas Poggi, BSC, Amsterdam
·       Saumil Merchant, Shell, India
·       Tilmann Rabl, DIMA, Toronto, Canada
·       Todor Ivanov, Goethe University, Germany
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