大数据环境下的数据存储:调查

A. Elomari, A. Maizate, L. Hassouni
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引用次数: 9

摘要

作为所有领域需要处理的数据量;科学的、专业的、社会的……数据的高速增长,对其管理和存储提出了越来越多的挑战。高度可伸缩基础架构的出现促进了存储管理技术的发展。但是,出现了许多问题,例如数据的一致性和可用性、环境的可伸缩性或对数据的竞争性访问。本文的目的是回顾、讨论和比较市场上现有的一些主要技术方向的主要特征,如谷歌文件系统(GFS)和IBM通用并行文件系统(GPFS),或者是开源系统,如Hadoop分布式文件系统(HDFS)、Blobseer和Andrew文件系统(AFS),以了解导致这些方向的需求和限制。对于每种情况,我们将讨论大数据存储管理的一组主要问题,以及如何解决这些问题以提供最佳的存储服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data storage in big data context: A survey
As data volumes to be processed in all domains; scientific, professional, social…etc., are increasing at a high speed, their management and storage raises more and more challenges. The emergence of highly scalable infrastructures has contributed to the evolution of storage management technologies. However, numerous problems have emerged such as consistency and availability of data, scalability of environments or yet the competitive access to data. The objective of this paper is to review, discuss and compare the main characteristics of some major technological orientations existing on the market, such as Google File System (GFS) and IBM General Parallel File System (GPFS) or yet on the open source systems such as Hadoop Distributed File System (HDFS), Blobseer and Andrew File System (AFS), in order to understand the needs and constraints that led to these orientations. For each case, we will discuss a set of major problems of big data storage management, and how they were addressed in order to provide the best storage services.
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