Scalable Network Migration Strategies: A Case Study Approach to Data Centre Consolidation in the Telecom Sector

International Journal of Engineering & Tech Development

Volume 1, Issue 1 (2025)
Authors

Laxmana Murthy Karaka1, Manikanth Sakuru2, Varun Bodepudi3
1Code Ace Solutions Inc, Software Engineer.
2JP Morgan Chase, Lead Software Engineer.
3Deloitte Consulting LLP, Senior Solution Specialist.

📄 Download PDF

Abstract

In fact, telecom companies have been compelled to change how they set up their data centres due to the need for faster services and constantly changing communications technologies. This paper discusses ways of upgrading networks so that more data centres can be accommodated in them. The way to make service improvements, optimize resources, and minimize network downtime is also discussed. We analyse how different assembly can be done, like phased migration, hybrid cloud deployment, and SDN for controlling architectures. We do this with the help of numerous real-life examples from the world of telecommunications. The study outlines some technical, operational, and organizational issues that surfaced during the migration process. It also provided strategic insight into how these concepts can be made feasible to adopt, enabling these companies to scale up and become better prepared for the next evolution. This study lets telecom companies know how to go about upgrading their networks while maintaining them strong, flexible, and affordable.

Keywords

Network Migration Data Centre Consolidation Telecom Infrastructure Scalable Architecture Hybrid Cloud Software-Defined Networking (SDN) IT Transformation Operational Efficiency Infrastructure Modernization Business Continuity

How to Cite This Article


Karaka, L. M., Sakuru, M., & Bodepudi, V. (2025). Scalable network migration strategies: A case study approach to data centre consolidation in the telecom sector. International Journal of Engineering & Tech Development, 1(1), 13-20.

References

[1] Rajiv, C., Mukund Sai, V. T., Venkataswamy Naidu, G., Sriram, P., & Mitra, P. (2022). Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic. J Contemp Edu Theo Artific Intel: JCETAI/102.

[2] Sandeep Kumar, C., Srikanth Reddy, V., Ram Mohan, P., Bhavana, K., & Ajay Babu, K. (2022). Efficient Machine Learning Approaches for Intrusion Identification of DDoS Attacks in Cloud Networks. J Contemp Edu Theo Artific Intel: JCETAI/101.

[3] Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2020). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153–164.DOI: 10.31586/jaibd.2022.1341

[4] Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340

[5] Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2022). Designing an Intelligent Cybersecurity Intrusion Identify Framework Using Advanced Machine Learning Models in Cloud Computing. Universal Library of Engineering Technology, (Issue).

[6] Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2022). Leveraging Artificial Intelligence Algorithms for Risk Prediction in Life Insurance Service Industry. Available at SSRN 5459694.

[7] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 2(3), 70-80.

[8] Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Tyagadurgam, M. S. V. Efficient Framework for Forecasting Auto Insurance Claims Utilizing Machine Learning Based Data-Driven Methodologies. International Research Journal of Economics and Management Studies IRJEMS, 1(2).

[9] Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H. S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of Applications, Challenges, and Innovations. International Journal of Emerging Research in Engineering and Technology, 3(3), 99-107.

[10] Narra, B., Vattikonda, N., Gupta, A. K., Buddula, D. V. K. R., Patchipulusu, H. H. S., & Polu, A. R. (2022). Revolutionizing Marketing Analytics: A Data-Driven Machine Learning Framework for Churn Prediction. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 112-121.

[11] Polu, A. R., Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., Vattikonda, N., & Gupta, A. K. BLOCKCHAIN TECHNOLOGY AS A TOOL FOR CYBERSECURITY: STRENGTHS, WEAKNESSES, AND POTENTIAL APPLICATIONS.

[12] Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2022). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153–164.DOI: 10.31586/jaibd.2022.1341

[13] Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340

[14] Gangineni, V. N., Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Tyagadurgam, M. S. V. (2023). AI-Enabled Big Data Analytics for Climate Change Prediction and Environmental Monitoring. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 71-79.

[15] Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Tyagadurgam, M. S. V., & Gangineni, V. N. (2023). Scalable Deep Learning Algorithms with Big Data for Predictive Maintenance in Industrial IoT. International Journal of AI, BigData, Computational and Management Studies, 4(1), 88-97.

[16] Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2023). Predictive models for early detection of chronic diseases in elderly populations: A machine learning perspective. Int J Comput Artif Intell, 4(1), 71-79.

[17] Polam, R. M. (2023). Predictive Machine Learning Strategies and Clinical Diagnosis for Prognosis in Healthcare: Insights from MIMIC-III Dataset. Available at SSRN 5495028.

[18] Gangineni, V. N., Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Tyagadurgam, M. S. V. (2023). AI-Enabled Big Data Analytics for Climate Change Prediction and Environmental Monitoring. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 71-79.

[19] Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Tyagadurgam, M. S. V., & Gangineni, V. N. (2023). Scalable Deep Learning Algorithms with Big Data for Predictive Maintenance in Industrial IoT. International Journal of AI, BigData, Computational and Management Studies, 4(1), 88-97.

[20] Polam, R. M. (2023). Predictive Machine Learning Strategies and Clinical Diagnosis for Prognosis in Healthcare: Insights from MIMIC-III Dataset. Available at SSRN 5495028.

[21] Bhumireddy, J. R. (2023). A Hybrid Approach for Melanoma Classification using Ensemble Machine Learning Techniques with Deep Transfer Learning Article in Computer Methods and Programs in Biomedicine Update. Available at SSRN 5667650.

[22] HK, K. (2020). Design of Efficient FSM Based 3D Network on Chip Architecture. INTERNATIONAL JOURNAL OF ENGINEERING, 68(10), 67-73.

[23] Krutthika, H. K. (2019, October). Modeling of Data Delivery Modes of Next Generation SOC-NOC Router. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.

[24] Ajay, S., Satya Sai Krishna Mohan G, Rao, S. S., Shaunak, S. B., Krutthika, H. K., Ananda, Y. R., & Jose, J. (2018). Source Hotspot Management in a Mesh Network on Chip. In VDAT (pp. 619-630).

[25] Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv preprint arXiv:1001.3781.

[26] Gopalakrishnan Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv e-prints, arXiv-1001.

[27] Krutthika H. K. & A.R. Aswatha. (2021). Implementation and analysis of congestion prevention and fault tolerance in network on chip. Journal of Tianjin University Science and Technology, 54(11), 213–231. https://doi.org/10.5281/zenodo.5746712

[28] Krutthika H. K. & A.R. Aswatha. (2020). FPGA-based design and architecture of network-on-chip router for efficient data propagation. IIOAB Journal, 11(S2), 7–25.

[29] Krutthika H. K. & A.R. Aswatha (2020). Design of efficient FSM-based 3D network-on-chip architecture. International Journal of Engineering Trends and Technology, 68(10), 67–73. https://doi.org/10.14445/22315381/IJETT-V68I10P212

[30] Krutthika H. K. & Rajashekhara R. (2019). Network-on-chip: A survey on router design and algorithms. International Journal of Recent Technology and Engineering, 7(6), 1687–1691. https://doi.org/10.35940/ijrte.F2131.037619