The Ultralight project: the network as an integrated and managed resource for data-intensive science

Looks at the UltraLight project which treats the network interconnecting globally distributed data sets as a dynamic, configurable, and closely monitored resource to construct a next-generation system that can meet the high-energy physics community's data-processing, distribution, access, and analysis needs

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Search and Discovery Tools for Astronomical On-line Resources and Services

A growing number of astronomical resources and data or information services are made available through the Internet. However valuable information is frequently hidden in a deluge of non-pertinent or non up-to-date documents. At a first level, compilations of astronomical resources provide help for selecting relevant sites. Combining yellow-page services and meta-databases of active pointers may be an efficient solution to the data retrieval problem. Responses generated by submission of queries to a set of heterogeneous resources are difficult to merge or cross-match, because different data providers generally use different data formats: new endeavors are under way to tackle this problem. We review the technical challenges involved in trying to provide general search and discovery tools, and to integrate them through upper level interfaces.Comment: 7 pages, 2 Postscript figures; to be published in A&A

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio

Modeling and Analysis of Scholar Mobility on Scientific Landscape

Scientific literature till date can be thought of as a partially revealed landscape, where scholars continue to unveil hidden knowledge by exploring novel research topics. How do scholars explore the scientific landscape , i.e., choose research topics to work on? We propose an agent-based model of topic mobility behavior where scholars migrate across research topics on the space of science following different strategies, seeking different utilities. We use this model to study whether strategies widely used in current scientific community can provide a balance between individual scientific success and the efficiency and diversity of the whole academic society. Through extensive simulations, we provide insights into the roles of different strategies, such as choosing topics according to research potential or the popularity. Our model provides a conceptual framework and a computational approach to analyze scholars' behavior and its impact on scientific production. We also discuss how such an agent-based modeling approach can be integrated with big real-world scholarly data.Comment: To appear in BigScholar, WWW 201

The future of shale

Master's Project (M.S.) University of Alaska Fairbanks, 2016This project examines the various drivers that led to the U.S. shale oil revolution in order to predict its place in the energy industry going forward and to analyze its effects on Alaska. The shale boom flooded the market with oil causing a dramatic decrease in crude oil prices in late 2014. With this price drop threatening to send Alaska into an economic recession, the future of shale should be of primary concern to all Alaskans as well as other entities that rely heavily on oil revenue. The primary driver leading to the shale revolution is technology. Advances in hydraulic fracturing, horizontal drilling, and 3D seismic mapping made producing shale oil and gas possible for the first time. New technologies like rotary steerable systems and measurements while drilling continue to make shale production more efficient, and technology will likely continue to improve. Infrastructure helps to explain why the shale revolution was mostly an American phenomenon. Many countries with shale formations have political infrastructure too unstable to risk shale investment. Capital infrastructure is a primary strength of the U.S. and also helps to explain why shale development didn't find its way up to Alaska despite having political stability. Financial infrastructure allowed oil companies to receive the funding necessary to quickly bring shale to the market. The final driver explored is crude oil prices. High oil prices helped spark the shale revolution, but with the recent price crash, there is uncertainty about its future. With production costs continually falling due to technology improvements and analysts predicting crude oil prices to stabilize above most project breakeven points, the future of shale looks bright.Introduction -- Shale & Alaska North Slope Crude Oil Prices -- Seeds of its own destruction? Technology -- Hydraulic Fracturing -- History of fracking -- Directional drilling -- History of drilling -- Benefits of directional drilling -- 3D seismic mapping -- Creating a shockwave -- Recording the data -- Interpreting the results -- The birth of a revolution -- Current/future developments -- Rotary steerable system -- Measurements while drilling -- Future developments. Infrastructure -- Political risk -- Financial markets -- Over investment -- Capital infrastructure. Crude prices -- The price crash -- Breakeven prices -- Future prices -- Alaska -- Conclusion -- Bibliography