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|Title:||Optimizing Hadoop Performance for Big Data Analytics in Smart Grid|
|Keywords:||configuration parameters;detrended fluctuation analysis;gene expression programming;Hadoop frameworks;open source implementation;phasor measurement unit (PMUs);rapid deployments;MapReduce;smart grid applications|
|Citation:||Mathematical Problems in Engineering, vol. 2017, Article ID 2198262, 11 pages, 2017|
|Abstract:||The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data fromPMUs. To that extent the Hadoop framework, an open source implementation of theMapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of theHadoop framework.This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
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