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A Multi-Optimization Technique for Improvement of Hadoop Performance with a Dynamic Job Execution Method Based on Artificial Neural Network

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Abstract

The improvement of Hadoop performance has received considerable attention from researchers in cloud computing fields. Most studies have focused on improving the performance of a Hadoop cluster. Notably, various parameters are required to configure Hadoop and must be adjusted to improve performance. This paper proposes a mechanism to improve Hadoop, schedule jobs, and allocate and utilize resources. Specifically, we present an improved ant colony optimization method to schedule jobs according to the job size and the time expected for execution. Priority is given to the job with the minimum data size and minimum response time. The resource usage and running jobs by data node are predicted using an artificial neural network, and job activity and resource usage are monitored using the resource manager. Moreover, we enhance the Hadoop Name node performance by adding an aggregator node to the default HDFS framework architecture. The changes involve four entities: the name node, secondary name node, aggregator nodes, and data nodes, where the aggregator node is responsible for assigning the jobs among the data node, and the Name node keeps tracking only the aggregator nodes. We test the overall scheme among Amazon EC2 and S3, and show the results of throughput and CPU response time for different data sizes. Finally, we show that the proposed approach shows significant improvement compare to native Hadoop and other approaches.

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Acknowledgements

This research was supported by the MIST (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (2017-0-00091). This work was supported by “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20174030201740).

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Jouf University, Al-Jawf, Saudi Arabia

    Rayan Alanazi

  2. Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

    Fawaz Alhazmi

  3. Computer Engineering Department (Department of Data Base Engineering), Dakook University, Yongin, South Korea

    Haejin Chung

  4. Applied Computer Engineering Department, Dankook University, Yongin, South Korea

    Yunmook Nah

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  1. Rayan Alanazi
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  2. Fawaz Alhazmi
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  4. Yunmook Nah
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Correspondence to Rayan Alanazi.

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Alanazi, R., Alhazmi, F., Chung, H. et al. A Multi-Optimization Technique for Improvement of Hadoop Performance with a Dynamic Job Execution Method Based on Artificial Neural Network. SN COMPUT. SCI. 1, 184 (2020). https://doi.org/10.1007/s42979-020-00182-3

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