The Oklahoma PetaStore: A Business Model for Big Data on a Small Budget

Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.) Pub Date : 2014-07-13 DOI:10.1145/2616498.2616548

Patrick Calhoun, David Akin, Joshua Alexander, Brett Zimmerman, Fred Keller, Brandon George, Henry Neeman

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引用次数: 2

Abstract

In the era of Big Data, research productivity can be highly sensitive to the availability of large scale, long term archival storage. Unfortunately, many mass storage systems are prohibitively expensive at scales appropriate for individual institutions rather than for national centers. Furthermore, a key issue is the set of circumstances under which researchers can, and are willing to, adopt a centralized technology that, in a pure cost recovery model, might be, or might appear to be, more expensive than what the research teams could build on their own. This paper examines a business model that addresses these concerns in a comprehensive manner, distributing the costs among a funding agency, the institution and the research teams, thereby reducing the challenges faced by each.

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俄克拉何马PetaStore:小预算下的大数据商业模式

在大数据时代，研究效率可能对大规模、长期档案存储的可用性高度敏感。不幸的是，许多大容量存储系统在适合单个机构而不是国家中心的规模上过于昂贵。此外，一个关键的问题是，在何种情况下，研究人员能够并且愿意采用一种集中的技术，在纯成本回收模型中，这种技术可能比研究团队自己开发的技术更昂贵，或者看起来更昂贵。本文研究了一种商业模式，它以一种全面的方式解决了这些问题，在资助机构、机构和研究团队之间分配成本，从而减少了每个机构面临的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)

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