Access Trends of In-network Cache for Scientific Data

Access Trends of In-network Cache for Scientific Data
复制标题

科学数据网内缓存访问趋势

DOI:
10.1145/3526064.3534110
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发表时间:
2022
期刊:
SNTA '22: Fifth International Workshop on Systems and Network Telemetry and Analytics
影响因子:
--
通讯作者:
Newman, Harvey
Newman, Harvey
中科院分区:
--
文献类型:
--
作者:
Han, Ruize;Sim, Alex;Wu, Kesheng;Monga, Inder;Guok, Chin;Würthwein, Frank;Davila, Diego;Balcas, Justas;Newman, Harvey

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科学合作的工作越来越依赖大量数据,其中许多合作采用分层系统将数据复制到全球用户社区。社区中的每个用户通常会选择不同的数据子集来执行其分析任务;然而,研究小组的成员经常致力于需要类似数据对象的相关研究主题。因此,可以进行大量数据共享。在这项工作中,我们研究了称为南加州 PB 级缓存的联合存储缓存的访问跟踪。通过研究该缓存系统的访问模式和减少网络流量的潜力,我们的目标是探索缓存使用的可预测性以及更通用的网络内数据缓存的潜力。我们的研究表明,这种分布式存储缓存能够在部分研究期间将网络流量减少 2.35 倍。我们进一步表明,机器学习模型可以预测缓存利用率,准确度为 0.88。这表明这种缓存使用是可预测的,这对于管理复杂的网络资源(例如网络内缓存)可能很有用。
Scientific collaborations are increasingly relying on large volumes of data for their work and many of them employ tiered systems to replicate the data to their worldwide user communities. Each user in the community often selects a different subset of data for their analysis tasks; however, members of a research group often are working on related research topics that require similar data objects. Thus, there is a significant amount of data sharing possible. In this work, we study the access traces of a federated storage cache known as the Southern California Petabyte Scale Cache. By studying the access patterns and potential for network traffic reduction by this caching system, we aim to explore the predictability of the cache uses and the potential for a more general in-network data caching. Our study shows that this distributed storage cache is able to reduce the network traffic volume by a factor of 2.35 during a part of the study period. We further show that machine learning models could predict cache utilization with an accuracy of 0.88. This demonstrates that such cache usage is predictable, which could be useful for managing complex networking resources such as in-network caching.
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