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Measuring data locality ratio in virtual MapReduce cluster using WorkflowSim
Wangsom, Peerasak; Lavangnananda, Kittichai; Bouvry, Pascal
2017In Proceedings of the 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2017 14th
Peer reviewed
 

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Keywords :
Computational modeling, Scheduling algorithms, Data models, Engines, Virtual environments, Prefetching, Tools
Abstract :
[en] The data locality is significant factor which has a direct impact on the performance of MapReduce framework. Several previous works have proposed alternative scheduling algorithms for improving the performance by increasing data locality. Nevertheless, their studies had focused the data locality on physical MapReduce cluster. As more and more deployment of MapReduce cluster have been on virtual environment, a more suitable evaluation of MapReduce cluster may be necessary. This study adopts a simulation based approach. Five scheduling algorithms were used for the simulation. WorkflowSim is extended by inclusion of three implemented modules to assess the new performance measure called `data locality ratio'. Comparison of their results reveals interesting findings. The proposed implementation can be used to assess `data locality ratio' and allows users prior to efficiently select and tune scheduler and system configurations suitable for an environment prior to its actual physical MapReduce deployment.
Research center :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Computer science
Author, co-author :
Wangsom, Peerasak
Lavangnananda, Kittichai
Bouvry, Pascal ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
yes
Language :
English
Title :
Measuring data locality ratio in virtual MapReduce cluster using WorkflowSim
Publication date :
July 2017
Event name :
14th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2017
Event date :
12-14 July, 2017
Audience :
International
Main work title :
Proceedings of the 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2017 14th
Publisher :
IEEE
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 15 January 2018

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