Open Access
Issue |
E3S Web Conf.
Volume 412, 2023
International Conference on Innovation in Modern Applied Science, Environment, Energy and Earth Studies (ICIES’11 2023)
|
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Article Number | 01091 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202341201091 | |
Published online | 17 August 2023 |
- C. Kacfah Emani, N. Cullot, and C. Nicolle, “Understandable Big Data: A survey,” Computer Science Review, vol. 17, pp. 70–81, Aug. (2015), doi: 10.1016/j.cosrev.2015.05.002. [CrossRef] [Google Scholar]
- A. Caggiano, “Cloud-based manufacturing process monitoring for smart diagnosis services,” International Journal of Computer Integrated Manufacturing, vol. 31, pp. 1–12, Jan. (2018), doi: 10.1080/0951192X.2018.1425552. [Google Scholar]
- S. Ibrahim, T. Phan, A. Carpen-Amarie, H.-E. Chihoub, D. Moise, and G. Antoniu, “Governing Energy Consumption in Hadoop through CPU Frequency Scaling: an Analysis,” Future Generation Computer Systems, vol. 54, Feb. (2015), doi: 10.1016/j.future.2015.01.005. [Google Scholar]
- D. M. Nascimento, M. Ferreira, and M. L. Pardal, “Does Big Data Require Complex Systems? A Performance Comparison Between Spark and Unicage Shell Scripts.” arXiv, Dec. 27, (2022). Accessed: Jun. 10, 2023. [Online]. Available: http://arxiv.org/abs/2212.13647 [Google Scholar]
- S. Chandrasekar, R. Dakshinamurthy, P. G. Seshakumar, P. Balasundaram, and C. Babu, A novel indexing scheme for efficient handling of small files in Hadoop Distributed File System. (2013), p. 8. doi: 10.1109/ICCCI.2013.6466147. [Google Scholar]
- V. Rao Chandakanna, “REHDFS: A random read/write enhanced HDFS,” Journal of Network and Computer Applications, vol. 103, pp. 85–100, Feb. (2018), doi: 10.1016/j.jnca.2017.11.017. [CrossRef] [Google Scholar]
- B. Mao, H. Jiang, S. Wu, Y. Fu, and L. Tian, “Read-Performance Optimization for Deduplication-Based Storage Systems in the Cloud,” ACM Transactions on Storage (TOS), vol. 10, Mar. (2014), doi: 10.1145/2512348. [Google Scholar]
- Liu Jiang, Bing Li, and Meina Song, “THE optimization of HDFS based on small files,” in 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), Beijing, China: IEEE, Oct. (2010), pp. 912–915. doi: 10.1109/ICBNMT.2010.5705223. [Google Scholar]
- N. Verstaevel, J. Boes, J. Nigon, D. d’Amico, and M.-P. Gleizes, Lifelong Machine Learning with Adaptive Multi-Agent Systems. (2017). doi: 10.5220/0006247302750286. [Google Scholar]
- M. Guériau, F. Armetta, S. Hassas, R. Billot, and N.-E. E. Faouzi, “A constructivist approach for a self-adaptive decision-making system: application to road traffic control,” presented at the 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Nov. (2016), p. 670. doi: 10.1109/ICTAI.2016.0107. [Google Scholar]
- S. Shamraj and P. J. Kulkarni, “Data Access Monitoring and Replication Control Management System for HDFS Clusters,” in 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India: IEEE, May (2018), pp. 2342–2345. doi: 10.1109/RTEICT42901.2018.9012567. [Google Scholar]
- R. Subramanyam, “HDFS Heterogeneous Storage Resource Management Based on Data Temperature,” in 2015 International Conference on Cloud and Autonomic Computing, Boston, MA, USA: IEEE, Sep. (2015), pp. 232–235. doi: 10.1109/ICCAC.2015.33. [Google Scholar]
- R. Kaushik, T. Abdelzaher, R. Egashira, and K. Nahrstedt, “Predictive data and energy management in GreenHDFS,” Jul. (2011), doi: 10.1109/IGCC.2011.6008563. [Google Scholar]
- Z. Cheng et al., ERMS: An elastic replication management system for HDFS. (2012), p. 40. doi: 10.1109/ClusterW.2012.25. [Google Scholar]
- R. Sanchez, “A Multi agent Simulation Framework on Small Hadoop Clusters”, Accessed: Jun. 10, (2023). [Online]. Available: https://www.academia.edu/19597670/A_Multi_agent_Simulation_Framework_on_Small_Hadoop_Clusters [Google Scholar]
- K. Shvachko, H. Kuang, S. Radia, and R. Chansler, “The Hadoop Distributed File System,” in 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), May (2010), pp. 1–10. doi: 10.1109/MSST.2010.5496972. [Google Scholar]
- Y. Tian and X. Yu, “Trustworthiness study of HDFS data storage based on trustworthiness metrics and KMS encryption,” in 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China: IEEE, Jan. (2022), pp. 962–966. doi: 10.1109/ICPECA51329.2021.9362537. [Google Scholar]
- M. Sais, N. Rafalia, and J. Abouchabaka, “Intelligent Approaches to Optimizing Big Data Storage and Management: REHDFS system and DNA Storage,” Procedia Computer Science, vol. 201, pp. 746–751, Jan. (2022), doi: 10.1016/j.procs.2022.03.101. [CrossRef] [Google Scholar]
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