Call for Papers: 7th Workshop on Scientific Cloud Computing (ScienceCloud) Co-Located with HPDC 2016 Wed June 1st, 2016. Kyoto, Japan. Web: http://www.irisa.fr/kerdata/sciencecloud/2016/ ------------------------------------------------------------------------------- IMPORTANT DATES Paper Submission Due: February 29, 2016 Notification of Acceptance: March 12, 2016 Camera Ready Version Due: March 27, 2016 Workshop: June 1st, 2016 ------------------------------------------------------------------------------- OVERVIEW Computational and Data-Driven Sciences have become the third and fourth pillar of scientific discovery in addition to experimental and theoretical sciences. Scientific Computing has already begun to change how science is done, enabling scientific breakthroughs through new kinds of experiments that would have been impossible only a decade ago. Today's Big Data science is generating datasets that are increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of the 21st century. The support for data intensive computing is critical to advance modern science as storage systems have exposed a widening gap between their capacity and their bandwidth by more than 10-fold over the last decade. There is a growing need for advanced techniques to manipulate, visualize and interpret large datasets. Scientific Computing is the key to solving .grand challenges. in many domains and providing breakthroughs in new knowledge, and it comes in many shapes and forms: high-performance computing (HPC) which is heavily focused on compute-intensive applications; high-throughput computing (HTC) which focuses on using many computing resources over long periods of time to accomplish its computational tasks; many-task computing (MTC) which aims to bridge the gap between HPC and HTC by focusing on using many resources over short periods of time; and data-intensive computing which is heavily focused on data distribution, data-parallel execution, and harnessing data locality by scheduling of computations close to the data. The 6th workshop on Scientific Cloud Computing (ScienceCloud) will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running these kinds of scientific computing workloads on Cloud Computing infrastructures. The ScienceCloud workshop will focus on the use of cloud-based technologies to meet new compute-intensive and data-intensive scientific challenges that are not well served by the current supercomputers, grids and HPC clusters. The workshop will aim to address questions such as: What architectural changes to the current cloud frameworks (hardware, operating systems, networking and/or programming models) are needed to support science? Dynamic information derived from remote instruments and coupled simulation, and sensor ensembles that stream data for real-time analysis are important emerging techniques in scientific and cyber-physical engineering systems. How can cloud technologies enable and adapt to these new scientific approaches dealing with dynamism? How are scientists using clouds? Are there scientific HPC/HTC/MTC workloads that are suitable candidates to take advantage of emerging cloud computing resources with high efficiency? Commercial public clouds provide easy access to cloud infrastructure for scientists. What are the gaps in commercial cloud offerings and how can they be adapted for running existing and novel eScience applications? What benefits exist by adopting the cloud model, over clusters, grids, or supercomputers? What factors are limiting clouds use or would make them more usable/efficient? This workshop encourages interaction and cross-pollination between those developing applications, algorithms, software, hardware and networking, emphasizing scientific computing for such cloud platforms. We believe the workshop will be an excellent place to help the community define the current state, determine future goals, and define architectures and services for future science clouds. ------------------------------------------------------------------------------- TOPICS OF INTEREST We invite the submission of original work that is related to the topics below. The papers can be either short (4 pages) position papers, or long (8 pages) research papers. Topics of interest include (in the context of Cloud Computing): Scientific application cases studies on Cloud infrastructure Performance evaluation of Cloud environments and technologies Fault tolerance and reliability in cloud systems Data-intensive workloads and tools on Clouds Use of programming models such as Map-Reduce and its implementations Storage cloud architectures I/O and Data management in the Cloud Workflow and resource management in the Cloud Use of cloud technologies (e.g., NoSQL databases) for scientific applications Data streaming and dynamic applications on Clouds Dynamic resource provisioning Many-Task Computing in the Cloud Application of cloud concepts in HPC environments or vice versa High performance parallel file systems in virtual environments Virtualized high performance I/O network interconnects Virtualization Distributed Operating Systems Many-core computing and accelerators (e.g. GPUs, MIC) in the Cloud Cloud security ------------------------------------------------------------------------------- SUBMISSION INSTRUCTIONS Authors are invited to submit papers with unpublished, original work of not more than 8 pages of double column text using single spaced 10 point size on 8.5 x 11 inch pages (including all text, figures, and references), as per ACM 8.5 x 11 manuscript guidelines (document templates can be found at http://www.acm.org/sigs/publications/proceedings-templates). Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library Papers conforming to the above guidelines can be submitted through the workshop's paper submission system: https://easychair.org/conferences/?conf=sciencecloud2016. ------------------------------------------------------------------------------- ORGANIZERS Kyle Chard, University of Chicago & Argonne National Laboratory, USA Bogdan Nicolae, IBM Research, Ireland Alexandru Costan, Inria/IRISA, France Dongfang Zhao, Pacific Northwest National Laboratory, USA STEERING COMMITTEE Ian Foster, University of Chicago & Argonne National Lab, USA Pete Beckman, University of Chicago & Argonne National Laboratory, USA Carole Goble, University of Manchester, UK Dennis Gannon, Microsoft Research, USA Robert Grossman, University of Chicago, USA Ed Lazowska, University of Washington & Computing Community Consortium, USA David O'Hallaron, Carnegie Mellon University, USA Jack Dongarra, University of Tennessee, USA Geoffrey Fox, Indiana University, USA Lavanya Ramakrishnan, Lawrence Berkeley National Laboratory, USA Yogesh Simmhan, Indian Institute of Science, Bangalore, India Gabriel Antoniu, INRIA, France Ioan Raicu, Illinois Institute of Technology, USA PROGRAM COMMITTEE Samer Al-Kiswany, University of Wisconsin - Madison Roy Campbell, University of Illinois at Urbana-Champaign David Chiu, University of Puget Sound Devarshi Ghoshal, Lawrence Berkeley National Laboratory Chathura Herath, Indiana University Marty Humphrey, University of Virginia Dan Katz, University of Chicago Thilo Kielmann, Vrije Universiteit Gregor Laszewski, Indiana University Shiyong Lu Wayne State University David Martin, Argonne National Laboratory Ruben Santiago Montero, Universidad Complutense de Madrid Josh Simons, VMWare Christine Morin, INRIA Pasquale Pagano, CNR-ISTI Beth Plale, Indiana University Ioan Raicu, Illinois Institute of Technology Omer Rana, Cardiff University Matei Ripeanu, The University of British Columbia Douglas Thain, University of Notre Dame Johan Tordsson, Umea University Vasudeva Varma, IIIT Hyderabad Yong Zhao, University of Electronic Science and Technology of China