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ScienceCloud 2017 : 8th Workshop on Scientific Cloud Computing


When Jun 27, 2017 - Jun 27, 2017
Where Washington DC, USA
Submission Deadline Mar 10, 2017
Notification Due Apr 5, 2017
Final Version Due Apr 5, 2017
Categories    cloud computing   scientific computing

Call For Papers

Call for Papers: 8th Workshop on Scientific Cloud Computing (ScienceCloud)
June 27, 2017. Washington DC, USA

Co-Located with HPDC 2017


Paper Submission: March 10, 2017
Acceptance Notification: April 5, 2017
Final Papers: April 12, 2017
Workshop: June 27, 2017


The 8th workshop on Scientific Cloud Computing (ScienceCloud) provides the
scientific community with a dedicated forum for discussing new research,
development, and deployment efforts in running all kinds of scientific
computing workloads, services, and applications on Cloud Computing
infrastructures. ScienceCloud focuses on the use of cloud-based technologies to
meet new compute-intensive and data-intensive scientific challenges that are
not well served by current supercomputers, grids, and HPC clusters. The
workshop aims 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? How can cloud technologies
enable and adapt to emerging scientific approaches that rely on remote sensors,
streaming data, coupled simulation, and real-time experiments? 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? What are the gaps in commercial cloud offerings and how
can they be adapted for running or hosting scientific applications and
services? 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, services, algorithms, software, hardware and
networking, emphasizing scientific computing for such cloud platforms.
ScienceCloud provides the ideal forum to help the community define the current
state, determine future goals, and define architectures and services for
future science clouds.


We invite the submission of original work that is related to the topics below.
Papers can be either short (4-page) position papers, or long (8-page) research

Topics of interest include (in the context of Cloud Computing):

- Scientific application or service cases studies on Cloud infrastructure
- Performance evaluation of Cloud environments and technologies
- Fault tolerance and reliability in cloud systems
- Data or compute-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


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
Submission implies the willingness of at least one of the authors
to register and present the paper.

Papers conforming to the above guidelines can be submitted through the
workshop's submission system:


- Bogdan Nicolae, IBM Research, Ireland
- Dongfang Zhao, University of Nevada, Reno, USA
- Kyle Chard, University of Chicago & Argonne National Laboratory, USA
- Alexandru Costan, Inria/IRISA, France

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