Kate Keahey, Ioan Raicu, Kyle Chard, and Bogdan Nicolae. "Guest Editors Introduction: Special Issue on Scientific Cloud Computing"
July 2015: 8 papers accepted (19.5% acceptance rate), to appear in October - December 2015 Issue
AutoElastic: Automatic Resource Elasticity for High Performance Applications in the Cloud
Application-Level Optimization of Big Data Transfers Through Pipelining, Parallelism and Concurrency
Dynamic and Fault-Tolerant Clustering for Scientific Workflows
Ensemble: A Tool for Performance Modeling of Applications in Cloud Data Centers
To Cloudify or Not to Cloudify: the Question for a Scientific Data Center
Cloud Computing for Earth Surface Deformation Analysis via Spaceborne Radar Imaging: a Case Study
OverFlow: Multi-Site Aware Big Data Management for Scientific Workflows on Clouds
Monetary Cost Optimizations for Hosting Workflow-as-a-Service in IaaS Clouds
August 2014: 41 papers submitted
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):
- 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
- Distributed Operating Systems
- Many-core computing and accelerators (e.g. GPUs, MIC) in the Cloud
- Cloud security
- Paper submission: July 31st, 2014 (AOE)
- First Round Decisions: September 30th, 2014
- Major Revisions Due (if needed): October 31st, 2014 (AOE)
- Final Decisions: December 1st, 2014
- Special Issue Date: December 2014
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 https://mc.manuscriptcentral.com/tcc-cs, and the "SI-ScienceCloud" should be selected.
OrganizationGuest Editors (email@example.com)
- Kate Keahey, Argonne National Laboratory, USA
- Ioan Raicu, Illinois Institute of Technology, USA
- Kyle Chard, University of Chicago, USA
- Bogdan Nicolae, IBM Research, Ireland
Editor in Chief
- Rajkumar Buyya, The University of Melbourne, Australia