Graph-Based Data Relevance Estimation for Large Storage Systems

V. Venkatesan, Taras Lehinevych, G. Cherubini, A. Glybovets, M. Lantz
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引用次数: 2

Abstract

In storage systems, the relevance of files to users can be taken into account to determine storage control policies to reduce cost, while retaining high reliability and performance. The relevance of a file can be estimated by applying supervised learning and using the metadata as features. However, supervised learning requires many training samples to achieve an acceptable estimation accuracy. In this paper, we propose a novel graph-based learning system for the relevance estimation of files using a small training set. First, files are grouped into different file-sets based on the available metadata. Then a parameterized similarity metric among files is introduced for each file-set using the knowledge of the metadata. Finally, message passing over a bipartite graph is applied for relevance estimation. The proposed system is tested on various datasets and compared with logistic regression.
基于图的大型存储系统数据相关性估计
在存储系统中,可以根据文件与用户的相关性来确定存储控制策略,在保证高可靠性和高性能的前提下,降低成本。可以通过应用监督学习和使用元数据作为特征来估计文件的相关性。然而,监督学习需要许多训练样本才能达到可接受的估计精度。在本文中,我们提出了一种新的基于图的学习系统,用于使用小训练集进行文件的相关性估计。首先,根据可用的元数据将文件分组到不同的文件集中。然后利用元数据知识为每个文件集引入参数化的文件间相似性度量。最后,应用二部图上的消息传递进行相关性估计。该系统在不同的数据集上进行了测试,并与逻辑回归进行了比较。
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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