Reference : Measuring data locality ratio in virtual MapReduce cluster using WorkflowSim
Scientific journals : Article
Engineering, computing & technology : Computer science
Measuring data locality ratio in virtual MapReduce cluster using WorkflowSim
Wangsom, Peerasak [> >]
Lavangnananda, Kittichai [> >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Proceedings of International Joint Conference on Computer Science and Software Engineering (JCSSE), 2017 14th
[en] Computational modeling, Scheduling algorithms, Data models, Engines, Virtual environments, Prefetching, Tools
[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.
University of Luxembourg: High Performance Computing - ULHPC

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