Cloud and Autonomic Computing Conference
The International Conference on Cloud and Autonomic Computing (CAC 2014)
Imperial College, London  September 8-12, 2014
 NEWS:
  Keynote details now available.
  Final program now available.
  Early registration extended to Aug 15.
  Venue info now available.
  Conference registration available.
 
 GENERAL CHAIR:
Geoffrey C. Fox
Indiana University, USA
 
 PROGRAM CO-CHAIRS:
Franck Cappello
ANL, UIUC, USA
Masoud Sadjadi
Florida International Univ., USA
 
 CONFERENCE DATES:
Abstract registration:
  March 31, 2014
  April 18, 2014
Paper submission due:
  April 7, 2014
  May 3, 2014 Midnight EST
Notification to authors:
  June 12, 2014
Final paper due:
  July 4, 2014
 
 FURTHER INFORMATION:
 
 SPONSORS:
CAC

IEEE


KEYNOTE

Scientific Big Data Analytics – Practice and Experience

Morris Riedel
Juelich Supercomputing Centre
Adjunct Associate Professor, University of Iceland

Abstract:

Data transfer, storage management, sharing, curation and most notably data analysis of often geographically dispersed large quantities of data of experiments, observations, or computational simulations become ever more important for science, research, industry and governments. Scientists and engineers that analyse these massive datasets require therefore reliable infrastructures as well as scalable tools in order to perform ‘scientific big data analytics (SBDA)’. This keynote will take stock of selected scientific and engineering use cases that take advantage of parallel machine learning algorithms (e.g. classification, clustering, regression) in combination with established statistical data mining methods in the light of new challenges faced with ‘big data’. It will critically review practice and experience of selected community approaches and thus address several important questions: Is big data always better data for analytics? Are big data analytics frameworks really providing the functionality they promise or scientists require? How can the scientific big data analytics process be properly structured? What is the role of the Research Data Alliance and Open Grid Forum in this context? Do we need a peer-review process for steering the scientific big data analytics applications and evolution when using valuable storage and compute resources?


Bio:

Dr. - Ing. Morris Riedel is an Adjunct Associate Professor at the School of Engineering and Natural Sciences of the University of Iceland. He received his PhD from the Karlsruhe Institute of Technology (KIT) and started the work in parallel and distributed systems in the field of scientific visualization and computational steering of e-science applications on large-scale HPC resources. He previously held various positions at the Juelich Supercomputing Centre in Germany. At this institute, he is the head of a scientific research group focussed on ‘High Productivity Data Processing’ as part of the Federated Systems and Data Division. Lectures given in universities such as the University of Iceland, University of Applied Sciences of Cologne and University of Technology Aachen (RWTH Aachen) include 'High Performance Computing & Big Data', ‘Statistical Data Mining', ‘Handling of large datasets’ and ‘Scientific and Grid computing’. His current research focusses on 'high productivity processing of big data' in the context of scientific computing applications. He is currently a co-chair of the ‘big data analytics interest group’ of the Research Data Alliance (RDA) and contributes to the Open Grid Forum (OGF) in selected areas.



KEYNOTE

Autonomics, Cyberinfrastructure Federation, and Software-Defined Environments for Science [Slides]

Manish Parashar
Professor of Computer Science, Rutgers University
AAAS Fellow
IEEE Fellow

Abstract:

Manish Parashar
Software-defined platforms, such as those enabled by Cloud services, provide new levels of flexibility that combined with autonomic capabilities can lead to very dynamic infrastructures that can adapt themselves to application and user needs. Such platforms can enable new formulations in science and engineering by opportunistically leveraging heterogeneous and loosely connected data and computing resources. In this talk I will explore how elastic software-defined execution based on autonomic federation of resources and management of applications can support such dynamic and data-driven workflows. I will also explore how such abstractions can potentially lead to new paradigms and practices in science and engineering. This talk is based on research that is part of the CometCloud project at the NSF Cloud and Autonomic Computing Center at Rutgers and at the Rutgers Discovery Informatics Institute.


Bio:

Manish Parashar is Professor of Computer Science at Rutgers University. He is also the founding Director of the Rutgers Discovery Informatics Institute (RDI2) and site Co-Director of the NSF Cloud and Autonomic Computing Center (CAC). His research interests are in the broad areas of Parallel and Distributed Computing and Computational and Data-Enabled Science and Engineering. Manish serves on the editorial boards and organizing committees of a large number of journals and international conferences and workshops, and has deployed several software systems that are widely used. He has also received a number of awards and is Fellow of AAAS, Fellow of IEEE/IEEE Computer Society and Senior Member of ACM. For more information please visit http://parashar.rutgers.edu/.



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