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

Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing
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Author(s): Richard S. Segall (Department of Computer and Information Technology, Neil Griffin College of Business, Arkansas State University, Jonesboro, USA), Jeffrey S. Cook (Independent Researcher, Paragould, USA) and Gao Niu (Department of Mathematics, Bryant University, Smithfield, USA)
Copyright: 2019
Volume: 2
Issue: 1
Pages: 40
Source title: International Journal of Fog Computing (IJFC)
Editor(s)-in-Chief: Sam Goundar (Victoria University of Wellington, New Zealand) and Kashif Munir (National College of Business Administration & Economics, Pakistan)
DOI: 10.4018/IJFC.2019010104

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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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