7th Workshop on Scientific Cloud Computing (ScienceCloud) 2016

Workshop to be held with ACM HPDC

June 1st 2016, Kyoto, Japan

Download CFP (.txt).


Schedule (Seminar Room)

Session 1: Keynote Address
13:00 - 13:10 Workshop Introduction
13:10 - 14:00 Keynote: Who is afraid of I/O? - Exploring I/O Challenges and Opportunities at the Exascale
Dr. Michela Taufer, University of Delaware

Abstract: Clear trends in the past and current petascale systems (i.e., Jaguar and Titan) and the new generation of systems that will transition us toward exascale (i.e., Aurora and Summit) outline how concurrency and peak performance are growing dramatically, however, I/O bandwidth remains stagnant. Next-generation systems are expected to deliver 7 to 10 times higher peak floating-point performance with only 1 to 2 times higher PFS bandwidth compared to the current generation. Data intensive applications, especially those exhibiting bursty I/O, must take this aspect into consideration and be more selective about what data is written to disk and how the data is written. In addressing the needs of these applications, can we take advantage of a rapidly changing technology landscape, including containerized environments, burst buffers, and in-situ/in-transit analytics? Are these technologies ready to transition these applications to exascale? In general, existing software components managing these technologies are I/O-ignorant, resulting in systems running the data intensive applications that exhibit contentions, hot spots, and poor performance. In this talk, we explore challenges when dealing with I/O-ignorant high performance computing systems and opportunities for integrating I/O awareness in these systems. Specifically, we present solutions that use I/O awareness (1) to make informed decisions on applications' allocations in containerized environments, (2) to reduce contentions in scheduling policies managing under provisioned systems with burst buffers, and (3) to mitigate data movements in data-intensive simulations. Our proposed solutions go beyond high performance computing and develop opportunities for interdisciplinary collaborations.
Session 2: Geo-Distributed Data Storage
Chair: Alexandru Costan (IRISA / INSA Rennes)
14:00- 14:30 Towards Efficient Location and Placement of Dynamic Replicas for Geo-Distributed Data Stores
Pierre Matri, Alexandru Costan, Gabriel Antoniu, Jesus Montes and Maria S. Perez
14:30 - 15:00 Managing Object Versioning in Geo-Distributed Object Storage Systems
Joao Neto, Vianney Rancurel and Vinh Tao
15:00 - 15:30 Break
Session 3: HPC and Big Data Convergence
Chair: Gabriel Antoniu (Inria)
15:30 - 16:00 Value-Based Resource Management in High-Performance Computing Systems
Dylan Machovec, Cihan Tunc, Nirmal Kumbhare, Bhavesh Khemka, Ali Akoglu, Salim Hariri and Howard Jay Siegel
16:00 - 16:30 A Workload Aware Storage Platform for Large Scale Computing Environments: Key Challenges and Proposed Direction
Benoit Tremblay, Karol Kozubal, Wubin Li and Chakri Padala
16:30 - 16:40 Workshop closing

Important dates


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):

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 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:


Steering Committee

Programme Committee