Storage tier-aware replicative data reorganization with prioritization for efficient workload processing

2018 ◽  
Vol 79 ◽  
pp. 618-629 ◽  
Author(s):  
Anton Spivak ◽  
Andrew Razumovskiy ◽  
Denis Nasonov ◽  
Alexander Boukhanovsky ◽  
Anton Redice
Keyword(s):  
Data Reorganization

Xscale: Online X-Code RAID-6 Scaling Using Lightweight Data Reorganization

2016 ◽  
Vol 27 (12) ◽  
pp. 3687-3700 ◽  
Author(s):  
Guangyan Zhang ◽  
Guiyong Wu ◽  
Yu Lu ◽  
Jie Wu ◽  
Weimin Zheng
Keyword(s):  
Data Reorganization

Problem Decomposition and Data Reorganization by a Clustering Technique

1972 ◽  
Vol 20 (5) ◽  
pp. 993-1009 ◽  
Author(s):  
William T. McCormick ◽  
Paul J. Schweitzer ◽  
Thomas W. White
Keyword(s):  
Problem Decomposition ◽  
Clustering Technique ◽  
Data Reorganization

Algorithm Development, Simulation Analysis and Parametric Studies for Data Allocation in Distributed Database Systems

2002 ◽  
pp. 157-189
Author(s):  
Amita Goyal Chin
Keyword(s):  
Simulation Analysis ◽  
Database Systems ◽  
Distributed Database ◽  
Database System ◽  
Data Allocation ◽  
Distributed Database Systems ◽  
Data Reorganization ◽  
Incremental Growth ◽  
Efficient Data ◽  
Distributed Database System

In a distributed database system, an increase in workload typically necessitates the installation of additional database servers followed by the implementation of expensive data reorganization strategies. We present the Partial REALLOCATE and Full REALLOCATE heuristics for efficient data reallocation. Complexity is controlled and cost minimized by allowing only incremental introduction of servers into the distributed database system. Using first simple examples and then, a simulator, our framework for incremental growth and data reallocation in distributed database systems is shown to produce near optimal solutions when compared with exhaustive methods.

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