Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing

R. Segall, J. Cook, G. Niu
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引用次数: 16

Abstract

Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical to application performance in such data-intensive computing systems. However, if existing resource management frameworks in these systems lack the support for storage management, this would cause unpredictable performance degradation when applications are under input/output (I/O) contention. Storage management of data-intensive systems is a challenge. Big Data plays a most major role in storage systems for data-intensive computing. This article deals with these difficulties along with discussion of High Performance Computing (HPC) systems, background for storage systems for data-intensive applications, storage patterns and storage mechanisms for Big Data, the Top 10 Cloud Storage Systems for data-intensive computing in today's world, and the interface between Big Data Intensive Storage and Cloud/Fog Computing. Big Data storage and its server statistics and usage distributions for the Top 500 Supercomputers in the world are also presented graphically and discussed as data-intensive storage components that can be interfaced with Fog-to-cloud interactions and enabling protocols.
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云计算和雾计算大数据密集型存储及其技术综述
由于数据的爆炸式增长和对数据处理的需求,计算系统正变得越来越数据密集型,因此存储管理对这种数据密集型计算系统的应用程序性能至关重要。但是,如果这些系统中的现有资源管理框架缺乏对存储管理的支持,那么当应用程序处于输入/输出(I/O)争用状态时,这将导致不可预测的性能下降。数据密集型系统的存储管理是一个挑战。大数据在数据密集型计算的存储系统中扮演着最重要的角色。本文将讨论高性能计算(HPC)系统、数据密集型应用的存储系统背景、大数据的存储模式和存储机制、当今世界数据密集型计算的十大云存储系统,以及大数据密集型存储与云/雾计算之间的接口。世界500强超级计算机的大数据存储及其服务器统计数据和使用分布也以图形形式呈现,并作为数据密集型存储组件进行讨论,这些组件可以与雾到云交互和启用协议进行接口。
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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