The bounds of the distributed data-intensive computing systems

2007 ◽  
Vol 2 (Supplement 1) ◽  
pp. 85-96 ◽  
Author(s):  
Antal Buza
Keyword(s):  
Distributed Data ◽  
Data Intensive Computing ◽  
Computing Systems ◽  
Data Intensive

G-Hadoop: MapReduce across distributed data centers for data-intensive computing

2013 ◽  
Vol 29 (3) ◽  
pp. 739-750 ◽  
Author(s):  
Lizhe Wang ◽  
Jie Tao ◽  
Rajiv Ranjan ◽  
Holger Marten ◽  
Achim Streit ◽  
...  
Keyword(s):  
Data Centers ◽  
Distributed Data ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Hadoop Mapreduce

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

2021 ◽  
pp. 112-153
Author(s):  
Richard S. Segall ◽  
Jeffrey S Cook ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Storage Systems ◽  
Fog Computing ◽  
Storage Management ◽  
Data Intensive Computing ◽  
Computing Systems ◽  
Application Performance ◽  
Data Intensive

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.

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

2019 ◽  
Vol 2 (1) ◽  
pp. 74-113 ◽  
Author(s):  
Richard S. Segall ◽  
Jeffrey S Cook ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Storage Systems ◽  
Fog Computing ◽  
Storage Management ◽  
Data Intensive Computing ◽  
Computing Systems ◽  
Application Performance ◽  
Data Intensive

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.

Dynamic power management in energy-aware computer networks and data intensive computing systems

2014 ◽  
Vol 37 ◽  
pp. 284-296 ◽  
Author(s):  
Ewa Niewiadomska-Szynkiewicz ◽  
Andrzej Sikora ◽  
Piotr Arabas ◽  
Mariusz Kamola ◽  
Marcin Mincer ◽  
...  
Keyword(s):  
Power Management ◽  
Computer Networks ◽  
Dynamic Power Management ◽  
Energy Aware ◽  
Data Intensive Computing ◽  
Computing Systems ◽  
Dynamic Power ◽  
Data Intensive
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