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Hadoop Dataset for Job Estimation in the Cloud with Limited Bandwidth

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Advances in Information and Communication (FICC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 652))

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Abstract

Hadoop MapReduce is a well-known open source framework for processing a large amount of data in a cluster of machines; it has been adopted by many organizations and deployed on-premise and on the cloud. MapReduce job execution time estimation and prediction are crucial for efficient scheduling, resource management, better energy consumption, and cost saving. In this paper, we present our new dataset of MapReduce job traces in a cloud environment with limited network bandwidth; we describe the process of generating and collecting the dataset in this paper. We believe that this dataset will help researchers develop new scheduling approaches and improve Hadoop MapReduce job performance.

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Notes

  1. 1.

    The dataset is available upon request from the corresponding author.

References

  1. Apache hadoop

    Google Scholar 

  2. Apache hadoop 2.10.1 – resourcemanager rest apis

    Google Scholar 

  3. Apache hadoop mapreduce historyserver – mapreduce history server rest apis

    Google Scholar 

  4. Dataproc image version list — dataproc documentation — google cloud

    Google Scholar 

  5. Dataproc — google cloud

    Google Scholar 

  6. Tpcx-bb express big data benchmark

    Google Scholar 

  7. Alapati, S.R.: Expert Hadoop Administration: Managing, Tuning, and Securing Spark, YARN, and HDFS, , 1st edn. Addison-Wesley Professional (2016)

    Google Scholar 

  8. Ceesay, S., Barker, A., Lin, Y.: Benchmarking and performance modelling of mapreduce communication pattern. In: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 127–134 (2019)

    Google Scholar 

  9. Heidari, S., Alborzi, M., Radfar, R., Afsharkazemi, M., Ghatari, A.: Big data clustering with varied density based on mapreduce. J. Big Data 6, 08 (2019)

    Article  Google Scholar 

  10. Kadirvel, S., Fortes, J.A.B.: Grey-box approach for performance prediction in map-reduce based platforms. In: 2012 21st International Conference on Computer Communications and Networks (ICCCN), pp. 1–9 (2012)

    Google Scholar 

  11. Khan, M., Jin, Y., Li, M., Xiang, Y., Jiang, C.: Hadoop performance modeling for job estimation and resource provisioning. IEEE Trans. Parallel Distrib. Syst. 27(2), 441–454 (2016)

    Article  Google Scholar 

  12. Singh, R., Kaur, P.: Analyzing performance of apache tez and mapreduce with hadoop multinode cluster on amazon cloud. J. Big Data 3, 10 (2016)

    Article  Google Scholar 

  13. Song, G., Meng, Z., Huet, F., Magoules, F., Yu, L., Lin, X.: A hadoop mapreduce performance prediction method. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp. 820–825 (2013)

    Google Scholar 

  14. Tariq, H., Al-Sahaf, H., Welch, I.: Modelling and prediction of resource utilization of hadoop clusters: a machine learning approach. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2019, pp. 93–100. Association for Computing Machinery, New York (2019)

    Google Scholar 

  15. White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc., 4th edn. (2015)

    Google Scholar 

  16. Zhang, Z., Cherkasova, L., Loo, B.T.: Benchmarking approach for designing a mapreduce performance model. In: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering, ICPE 2013, pp. 253–258. Association for Computing Machinery, New York (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory of Intelligent Systems and Applications, Department of Computer Science, Faculty of Sciences and Technologies, University of Sidi Mohammed Ben Abdellah, Fez, Morocco

    Mohammed Bergui & Said Najah

  2. Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Nikola S. Nikolov

Authors
  1. Mohammed Bergui
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  2. Nikola S. Nikolov
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  3. Said Najah
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Corresponding author

Correspondence to Mohammed Bergui .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Bergui, M., Nikolov, N.S., Najah, S. (2023). Hadoop Dataset for Job Estimation in the Cloud with Limited Bandwidth. In: Arai, K. (eds) Advances in Information and Communication. FICC 2023. Lecture Notes in Networks and Systems, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-28073-3_24

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