Scibox: Online Sharing of Scientific Data via the Cloud

Jian Huang, Xuechen Zhang, G. Eisenhauer, K. Schwan, M. Wolf, S. Ethier, S. Klasky
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引用次数: 7

Abstract

Collaborative science demands global sharing of scientific data. But it cannot leverage universally accessible cloud-based infrastructures like Drop Box, as those offer limited interfaces and inadequate levels of access bandwidth. We present the Scibox cloud facility for online sharing scientific data. It uses standard cloud storage solutions, but offers a usage model in which high end codes can write/read data to/from the cloud via the APIs they already use for their I/O actions. With Scibox, data upload/download volumes are controlled via Data Reduction-functions stated by end users and applied at the data source, before data is moved, with further gains in efficiency obtained by combining DR-functions to move exactly what is needed by current data consumers. We evaluate Scibox with science applications and their representative data analytics - the GTS fusion and the combustion image processing - demonstrating the potential for ubiquitous data access with substantial reductions in network traffic.
Scibox:通过云进行科学数据的在线共享
协作科学要求全球共享科学数据。但它无法利用Drop Box等普遍可访问的基于云的基础设施,因为这些基础设施提供的接口有限,访问带宽水平不足。我们提出了用于在线共享科学数据的Scibox云设施。它使用标准的云存储解决方案,但提供了一个使用模型,在这个模型中,高端代码可以通过它们已经用于I/O操作的api向云写入/读取数据。在Scibox中,数据上传/下载量通过最终用户指定的数据还原功能进行控制,并在数据移动之前应用于数据源,通过结合dr功能来移动当前数据消费者所需的数据,进一步提高了效率。我们用科学应用及其代表性数据分析(GTS融合和燃烧图像处理)来评估Scibox,展示了在大幅减少网络流量的情况下无处不在的数据访问的潜力。
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