Paper Info
Title
Dynamic Data Partitioning and Virtual Machine Mapping: Efficient Data Intensive Computation
Abstract
Big data refers to data that is so large that it exceeds the processing capabilities of traditional systems. Big data can be awkward to work and the storage, processing and analysis of big data can be problematic. MapReduce is a recent programming model that can handle big data. MapReduce achieves this by distributing the storage and processing of data amongst a large number of computers (nodes). However, this means the time required to process a MapReduce job is dependent on whichever node is last to complete a task. This problem is exacerbated by heterogeneous environments. In this paper we propose a method to improve MapReduce execution in heterogeneous environments. This is done by dynamically partitioning data during the Map phase and by using virtual machine mapping in the Reduce phase in order to maximize resource utilization.
Year
DOI
Venue
2013
10.1109/CloudCom.2013.134
CloudCom (2)
Keywords
Field
DocType
recent programming model,mapreduce job,processing capability,dynamic data partitioning,efficient data intensive computation,virtual machine mapping,map phase,dynamically partitioning data,reduce phase,large number,mapreduce execution,heterogeneous environment,big data,virtual machines
Virtual machine,Programming paradigm,Data-intensive computing,Computer science,Parallel computing,Dynamic data,Storage management,Big data,Cloud computing,Computation,Distributed computing
Conference
ISSN
Citations 
PageRank 
2330-2194
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Kenn Slagter1584.00
Ching-Hsien Hsu21121125.53
Yeh-Ching Chung398397.16