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.

scholarly journals Workflow Scheduling Under Secure Cloud Environment Using MPSO-SA

2019 ◽  
Vol 8 (3) ◽  
pp. 7440-7446
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Security Requirements ◽  
Distributed Data ◽  
Computing Environment ◽  
Data Intensive Computing ◽  
Swarm Optimization ◽  
Data Intensive ◽  
The Cost ◽  
Cyclic Term

In the distributed data-intensive computing environment, relegating certain assignments to specific machines in a protected way is a major test for the employment planning issue. The unpredictability of this issue increments with the size of the activity and it is hard to understand viably. A few metaheuristic calculations including particle swarm optimization (PSO) strategy and variable neighborhood particle swarm optimization VNPSO) system are utilized to tackle the employment planning issue in distributed computing. While allocating assignments to the machines, to fulfill the security requirements and to limit the cost capacity, we proposed an altered PSO with a scout adjustment (MPSO-SA) calculation which utilized a cyclic term called change administrator to get the best cost capacity. The exhibition of the proposed MPSO-SA booking component is contrasted and the Genetic calculation (GA), PSO and VNPSO systems and the exploratory outcome demonstrate that the proposed technique diminishes the likelihood of hazard with security requirements and it has preferable intermingling property over the current conventions.

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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