面向数据密集型科学应用的智能互联网级缓存服务

Yubo Qin, Anthony Simonet, Philip E. Davis, Azita Nouri, Zhe Wang, M. Parashar, I. Rodero
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引用次数: 5

摘要

共享设施(如大型观测设施)提供的数据和服务已成为许多科学和工程学科的科学见解和发现的重要推动力。对于设施来说,确保令人满意的服务质量可能具有挑战性,因为它们的位置偏远,仪器、观测站和用户的分布性质,以及数据量和速率的快速增长。本研究探讨了如何利用设施使用模式的知识,结合新兴的网络基础设施来提高其性能、可用性和科学影响。我们提出了一个具有智能互联网规模缓存的框架,增强了预取和数据放置策略,以提高科学设施的数据传输性能。我们的评估基于NSF海洋观测站计划,表明我们的框架能够预测用户请求,并将网络上的数据移动减少56%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a Smart, Internet-Scale Cache Service for Data Intensive Scientific Applications
Data and services provided by shared facilities, such as large-scale observing facilities, have become important enablers of scientific insights and discoveries across many science and engineering disciplines. Ensuring satisfactory quality of service can be challenging for facilities, due to their remote locations and to the distributed nature of the instruments, observatories, and users, as well as the rapid growth of data volumes and rates. This research explores how knowledge of the facilities usage patterns, coupled with emerging cyberinfrastructures can be leveraged to improve their performance, usability, and scientific impact. We propose a framework with a smart, internet-scale cache augmented with prefetching and data placement strategies to improve data delivery performance for scientific facilities. Our evaluations, which are based on the NSF Ocean Observatories Initiative, demonstrate that our framework is able to predict user requests and reduce data movements by more than 56% across networks.
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