DataSys: Data-Intensive Distributed Systems LaboratoryData-Intensive Distributed Systems Laboratory

Illinois Institute of Technology
Department of Computer Science

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IEEE Transactions on Cloud Computing

Special Issue on Scientific Cloud Computing




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. It 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. Today’s “Big Data” trend 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. Not surprisingly, it becomes increasingly difficult to design and operate large scale systems capable of addressing these grand challenges.

This journal Special Issue on Scientific Cloud Computing in the IEEE Transaction on Cloud Computing 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. This special issue 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 special issue 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 special issue encourages interaction and cross-pollination between those developing applications, algorithms, software, hardware and networking, emphasizing scientific computing for such cloud platforms. We believe the special issue will be an excellent venue to help the community define the current state, determine future goals, and define architectures and services for future science clouds. 


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

Important Dates

Paper Submission

Authors are invited to submit papers with unpublished, original work to the IEEE Transaction on Cloud Computing, Special Issue on Scientific Cloud Computing. If the paper is extended from a workshop or conference paper, it needs to contain at least 50% new material with "brand" new ideas and results. The paper should be in the TCC format -- 14 double column pages or 30 single column pages (Note: All regular paper page limits include references and author biographies). Please note that double column will translate more readily into the final publication format. A double column page is defined as a 7.875" x 10.75" page with 10-point type, 12-point vertical spacing, and 1/2 inch margins. A single column page is defined as an 8.5" x 11" page with 12-point type and 24-point vertical spacing, containing approximately 250 words. All margins should be one inch (top, bottom, right and left). These length limits are taking into account reasonably-sized figures and references.


These templates are meant to aid you in preparing a draft of your manuscript for peer-review as well as to prepare final publication materials that will closely translate into the final publication format. This does not mean that the published paper will appear in this format. The published paper will appear as formatted by IEEE Computer Society publication staff. Full specifications on the IEEE Computer Society style can be found in our Style Guide PDF

Papers should be submitted to, and the "SI-ScienceCloud" should be selected.


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Editor in Chief