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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Md. Armanur Rahman
Multimedia University
Computer Science & IT Electrical & Electronics Engineering

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A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance Md. Armanur Rahman; J. Hossen; Venkataseshaiah C; CK Ho; Tan Kim Geok; Aziza Sultana; Jesmeen M. Z. H.; Ferdous Hossain
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.262 KB) | DOI: 10.11591/ijece.v8i3.pp1854-1862

Abstract

The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement.
A smart method for spark using neural network for big data Md. Armanur Rahman; J. Hossen; Aziza Sultana; Abdullah Al Mamun; Nor Azlina Ab. Aziz
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2525-2534

Abstract

Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of distributed memory to process big data. As the performance of the spark is mostly being affected by the spark predominant configuration parameters, it is challenging to achieve the optimal result from spark. The current practice of tuning the parameters is ineffective, as it is performed manually. Manual tuning is challenging for large space of parameters and complex interactions with and among the parameters. This paper proposes a more effective, self-tuning approach subject to a neural network called Smart method for spark using neural network for big data (SSNNB) to avoid the disadvantages of manual tuning of the parameters. The paper has selected five predominant parameters with five different sizes of data to test the approach. The proposed approach has increased the speed of around 30% compared with the default parameter configuration.
Towards machine learning-based self-tuning of Hadoop-Spark system Md. Armanur Rahman; Abid Hossen; J. Hossen; Venkataseshaiah C; Thangavel Bhuvaneswari; Aziza Sultana
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp1076-1085

Abstract

Apache Spark is an open source distributed platform which uses the concept of distributed memory for processing big data. Spark has more than 180 predominant configuration parameter. Configuration settings directly control the efficiency of Apache spark while processing big data, to get the best outcome yet a challenging task as it has many configuration parameters.  Currently, these predominant parameters are tuned manually by trial and error. To overcome this manual tuning problem in this paper proposed and developed a self-tuning approach using machine learning. This approach can tune the parameter value when it’s required. The approach was implemented on Dell server and experiment was done on five different sizes of the dataset and parameter. A comparison is provided to highlight the experimented result of the proposed approach with default Spark configuration system. The results demonstrate that the execution is speeded-up by about 33% (on an average) compared to the default configuration.
Co-Authors Abdullah Al Mamun Abid Hossen Aziza Sultana CK Ho Ferdous Hossain J. Hossen Jesmeen M. Z. H. Nor Azlina Ab. Aziz Tan Kim Geok Thangavel Bhuvaneswari Venkataseshaiah C