Autors: Georgiev S., Nikolova, K. S.
Title: Proposal Of A Modular Framework For Building Network Infrastructure For Massively Scalable Data Centers For Private Cloud And AI Workloads
Keywords: Data Center, Load Balancing, Machine Learning, Private Cloud, RDMA, Virtualization

Abstract: In this paper, a modular framework for building network infrastructure for massively scalable data centers is proposed and tested. The framework is designed to be highly modular and flexible to allow a streamlined decision making process for deploying data center networking. The hierarchical nature of the framework provides decision makers with a clear dependency map between technologies at different layers and how they interact with each other. The different technologies utilized in different layers of the Open System Interconnect (OSI) model are presented. The problems of their inter-operability and design issues are pointed out as problems which the proposed framework aims to fix. Finally, it is demonstrated how the framework can be utilized, and a tree-based structure is generated at the end of the process.

References

  1. S. Begam, S. M, and S. Nagalingam, "Load balancing in dcn servers through sdn machine learning algorithm," Feb. 2021.
  2. S. Gawande and S. Gorde, "Hybrid cloud architectures: Balancing the benefits of public and private clouds," International Journal of Research in Engineering Technology, vol. 9, pp. 11-14, Oct. 2024.
  3. L. Korada and V. Sikha, "Why are large enterprises building private clouds after their journey on public clouds?" pp. 49-52, Feb. 2024.
  4. G. Grigoryan and M. Kwon, "Towards greener data centers via programmable data plane," pp. 62-67, Jun. 2023.
  5. E. Mahdi, A. Al-Askery, A. Hussein Ali, and S. Ghasemi, "A survey of datacenter and optical data center networks," Journal of Techniques, vol. 6, pp. 66-75, Sep. 2024.
  6. J. Li, F. Marchi, Y. Lei, R. Joshi, B. Chandrasekaran, and Y. Xia, "Unlocking diversity of fast-switched optical data center networks with unified routing," Nov. 2024.
  7. Y. Lei, F. Marchi, J. Li, R. Joshi, B. Chandrasekaran, and Y. Xia, "Lighthouse: An open research framework for optical data center networks," Nov. 2024.
  8. Z. Xie, D. Sanchez-Jacome, L. Torrijos-Moran, and D. Perez-Lopez, "Software-defined optical networking applications enabled by programmable integrated photonics," Journal of Optical Communications and Networking, vol. 16, Jun. 2024.
  9. J. Liu and L. Jiang, "Quantum data center: Perspectives," IEEE Network, vol. PP, pp. 1-1, Sep. 2024.
  10. H. Shapourian, E. Kaur, T. Sewell, J. Zhao, M. Kilzer, R. Kompella, and R. Nejabati, "Quantum data center infrastructures: A scalable architectural design perspective," Jan. 2025.
  11. K. Kant, "Data center evolution: A tutorial on state of the art, issues, and challenges," Computer Networks, vol. 53, pp. 2939-2965, Dec. 2009.
  12. K. Campbell, A. Lawey, and M. Razavi, "Quantum data centres: a simulation-based comparative noise analysis," Quantum Science and Technology, vol. 10, no. 1, p. 015052, Dec. 2024.
  13. C. Terzi and I. Korpeoglu, "60 ghz wireless data center networks: A survey," Computer Networks, vol. 185, p. 107730, 2021.
  14. A. Celik, B. Shihada, and M.-S. Alouini, "Wireless data center networks: Advances, challenges, and opportunities," ArXiv, vol. abs/1811.11717, 2018.
  15. D. R. Asani and C. Reddy, "Deploying own private cloud," Intern. Journal of Science and Research (IJSR), vol. 13, pp. 1356-1362, Jun. 2024.
  16. S. Fawzi and N. Din, "Leveraging sdn for scalable and sustainable fat tree networks: a multi-objective performance and energy efficiency evaluation of an 8-pod fat tree data center," Edelweiss Applied Science and Technology, vol. 9, pp. 218-230, Jun. 2025.
  17. P. Gupta and V. Pathak, "A minimum spanning tree-based routing technique of fat tree for efficient data center networking," Mathematical Statistician and Engineering Applications, vol. 71, Jun. 2022.
  18. V. Çapali, K. Sümer, and M. Ersoy, "Redundant data center planning for universities," Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 17, pp. 607-625, Dec. 2024.
  19. M. Macktoobian, Z. Shu, and Q. Zhao, "Traffic divergence theory: A formalism for network traffic dynamics analysis," IEEE Access, vol. PP, pp. 1-1, Jan. 2024.
  20. D. Kutner and I. Stewart, "Reconfigurable routing in data center networks," pp. 117-130, Dec. 2024.
  21. B. Chang, J. Mogul, R. Wang, M. Zhang, and A. Akella, "Do data center network metrics predict application-facing performance?" Nov. 2024.
  22. Q. Shen, J. Zheng, and P. Chow, "Rifl: a reliable link layer network protocol for data center communication," Journal of Optical Communications and Networking, vol. 14, Jan. 2022.
  23. O. Tareq and Q. Ali, "Tcp for data centers: Short flows and incast," International Journal of Advanced Natural Sciences and Engineering Researches, vol. 8, pp. 100-107, Dec. 2024.
  24. J. Xu, Z. Wang, F. Yang, N. Kang, Z. Ma, G. Yuan, G. Tan, and N. Sun, "Fncc: Fast notification congestion control in data center networks," pp. 127-137, Aug. 2024.
  25. M. Maliha, G. Habibi, and M. Atiquzzaman, "A survey on congestion control and scheduling for multipath tcp: Machine learning vs classical approaches," pp. 49-61, Sep. 2023.
  26. T. Gao, X. Ma, S. Narreddy, E. Luo, S. Chien, and M. Honda, "The case for transport-level encryption in datacenter networks," Jun. 2024.
  27. S. Mohamed, T. El-Gorashi, and J. Elmirghani, "A survey of big data machine learning applications optimization in cloud data centers and networks," Oct. 2019.
  28. M. Elbtity, P. Chandarana, and R. Zand, "Flex-tpu: A flexible tpu with runtime reconfigurable dataflow architecture," Jul. 2024.

Issue

International Conference on Systems, Signals, and Image Processing, pp. 1-5, 2025, Macedonia, https://doi.org/10.1109/IWSSIP66997.2025.11151866

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus