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Pan, X., Luo, Z., & Zhou , L. (2023). Navigating the Landscape of Distributed File Systems: Architectures, Implementations, and Considerations. Innovations in Applied Engineering and Technology, 2(1), 1–12. https://doi.org/10.62836/iaet.v2i1.157

Navigating the Landscape of Distributed File Systems: Architectures, Implementations, and Considerations

 Distributed File Systems (DFS) have emerged as sophisticated solutions for efficient file storage and management across interconnected computer nodes. The main objective of DFS is to achieve flexible, scalable, and resilient file storage management by dispersing file data across multiple interconnected computer nodes, enabling users to seamlessly access and manipulate files distributed across diverse nodes. This article provides an overview of DFS, its architecture, classification methods, design considerations, challenges, and common implementations. Common DFS implementations discussed include NFS, AFS, GFS, HDFS, and CephFS, each tailored to specific use cases and design goals. Understanding the nuances of DFS architecture, classification, and design considerations is crucial for developing efficient, stable, and secure distributed file systems to meet diverse user and application needs in modern computing environments.

distributed file system (DFS); architecture; NameNode; DataNodes

References

  1. Mahajan S, Shah S. Distributed Computing; Oxford University Press: New York, NY, USA, 2010.
  2. Levy E, Silberschatz A. Distributed File Systems: Concepts and Examples. ACM Computing Surveys (CSUR) 1990; 22(4): 321–374. DOI: https://doi.org/10.1145/98163.98169
  3. Feiyang C, Luo Z, Xu Y, Ke D. Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis. 2019; arXiv:1904.08138.
  4. Luo Z, Xu H, Chen F. Audio Sentiment Analysis by Heterogeneous Signal Features Learned from Utterance-Based Parallel Neural Network. In Proceedings of the AAAI 2018 Conference, New Orleans, LA, USA, 2–7 February 2018. DOI: https://doi.org/10.29007/7mhj
  5. Luo Z. Knowledge-Guided Aspect-Based Summarization. In Proceedings of the 2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI), Shanghai, China, 23–25 June 2023. DOI: https://doi.org/10.1109/CCCAI59026.2023.00012
  6. Luo Z, Zeng X, B, Xu M. Deep Learning-Based Strategy for Macromolecules Classification With Imbalanced Data From Cellular Electron Cryotomography. In 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019. DOI: https://doi.org/10.1109/IJCNN.2019.8851972
  7. Luo Z, Xu H, Chen F. Utterance-Based Audio Sentiment Analysis Learned By a Parallel Combination of cnn and lstm. 2018; arXiv:1811.08065.
  8. Dooley K. Designing Large-Scale LANs; O'Reilly Media, Inc: Sebastopol, CA, USA, 2002
  9. Sandbaerg R. Design and Implementation or the SUD Network File system. Sun Microsystems. Innovations in Internetworking 2002; 379.
  10. Vogels W. Eventually Consistent. Communications of the ACM 2009; 52(1): 40–44. DOI: https://doi.org/10.1145/1435417.1435432
  11. Tobbicke R. Distributed File Systems: Focus on Andrew File System/Distributed File Service (AFS/DFS). Proceedings Thirteenth IEEE Symposium on Mass Storage Systems. Toward Distributed Storage and Data Management Systems. Annecy, France, 12–16 June 1994.
  12. Ghemawat S, Gobioff H, Leung S-T. The Google File System. ACM SIGOPS Operating Systems Review 2003; 37(5): 29–43. DOI: https://doi.org/10.1145/1165389.945450
  13. Shvachko K, Kuang H, Radia S, Chansler R. The Hadoop Distributed File System. In Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), Incline Village, NV, USA, 3–7 May 2010. DOI: https://doi.org/10.1109/MSST.2010.5496972
  14. Parekh A, Gaur UK, Garg V. Analytical Modelling of Distributed File Systems (GlusterFS and CephFS). In Reliability, Safety and Hazard Assessment for Risk-Based Technologies; Springer: Singapore, 2020. DOI: https://doi.org/10.1007/978-981-13-9008-1_18
  15. Zhou Y, Osman A, Willms M, Kunz A, Philipp S, Blatt J, Eul S. Semantic Wireframe Detection. Available online: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ndt.net/article/dgzfp2023/papers/P17.pdf (accessed on 23 March2024).

Supporting Agencies

  1. Funding: Not applicable.