The Role of Network Virtualization Strategy in Enhancing Scalability and Cost Efficiency in Cloud Data Centers
Keywords:
Network virtualization strategy; cloud data centers; scalability; cost efficiency; software-defined networking; infrastructure optimization.Abstract
This study aimed to examine the role of network virtualization strategy in enhancing scalability and cost efficiency in cloud data centers. This applied quantitative study used a descriptive-correlational design. The statistical population consisted of cloud infrastructure specialists, network engineers, virtualization administrators, data center managers, IT project managers, and system architects working in cloud data centers and technology-based organizations in Tehran. A total of 214 participants were selected through purposive sampling based on their professional experience with cloud infrastructure, network virtualization, software-defined networking, or data center management. Data were collected using a structured questionnaire measuring network virtualization strategy, scalability, and cost efficiency. The validity of the questionnaire was confirmed through expert review, and its reliability was assessed using Cronbach’s alpha. Data were analyzed using descriptive statistics, Pearson correlation, multiple regression analysis, and structural equation modeling with SPSS and AMOS software. The inferential findings showed that network virtualization strategy had a positive and significant correlation with scalability (r = 0.68, p < 0.01) and cost efficiency (r = 0.63, p < 0.01). Scalability also had a positive and significant correlation with cost efficiency (r = 0.59, p < 0.01). Regression analysis indicated that network virtualization strategy significantly predicted scalability (β = 0.68, p < 0.001) and explained 46% of its variance. It also significantly predicted cost efficiency (β = 0.63, p < 0.001) and explained 40% of its variance. Structural equation modeling confirmed the proposed model, showing significant direct effects of network virtualization strategy on scalability (β = 0.69, p < 0.001) and cost efficiency (β = 0.61, p < 0.001), as well as a significant direct effect of scalability on cost efficiency (β = 0.28, p < 0.001). The findings indicate that network virtualization strategy is a key technological and managerial capability for improving the scalability and cost efficiency of cloud data centers.
References
[1] A. Blenk and W. Kellerer, "Network Virtualization and Network Hypervisors," pp. 1-11, 2022, doi: 10.1002/047134608x.w8428.
[2] L. Rosa, L. Foschini, and A. Corradi, "Empowering Cloud Computing With Network Acceleration: A Survey," Ieee Communications Surveys & Tutorials, vol. 26, no. 4, pp. 2729-2768, 2024, doi: 10.1109/comst.2024.3377531.
[3] R. Chaudhary, G. S. Aujla, N. Kumar, and P. K. Chouhan, "A Comprehensive Survey on Software‐defined Networking for Smart Communities," International Journal of Digital & Analog Cabled Systems, vol. 38, no. 1, 2022, doi: 10.1002/dac.5296.
[4] D. Pratiba, R. K. Pattar, and V. Reddy, "Functional Segments and Software Defined Trends in Enterprise Networks," Indonesian Journal of Electrical Engineering and Computer Science, vol. 31, no. 2, p. 957, 2023, doi: 10.11591/ijeecs.v31.i2.pp957-967.
[5] T. Malbašić, P. D. Bojović, Ž. Bojović, J. Šuh, and D. Vujošević, "Hybrid SDN Networks: A Multi-Parameter Server Load Balancing Scheme," Journal of Network and Systems Management, vol. 30, no. 2, 2022, doi: 10.1007/s10922-022-09642-y.
[6] Z. Qin, "SD-WAN for Bandwidth and Delay Improvements on the Internet," SHS Web of Conferences, vol. 144, p. 02004, 2022, doi: 10.1051/shsconf/202214402004.
[7] S. Bharany et al., "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, vol. 14, no. 10, p. 6256, 2022, doi: 10.3390/su14106256.
[8] A. Katal, S. Dahiya, and T. Choudhury, "Energy Efficiency in Cloud Computing Data Centers: A Survey on Software Technologies," Cluster Computing, vol. 26, no. 3, pp. 1845-1875, 2022, doi: 10.1007/s10586-022-03713-0.
[9] K. Öztoprak, Y. K. Tuncel, and İ. Bütün, "Technological Transformation of Telco Operators Towards Seamless IoT Edge-Cloud Continuum," Sensors, vol. 23, no. 2, p. 1004, 2023, doi: 10.3390/s23021004.
[10] L. Nkenyereye, L. Nkenyereye, and J.-W. Jang, "Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey," Electronics, vol. 12, no. 9, p. 2066, 2023, doi: 10.3390/electronics12092066.
[11] A. Arulappan, G. Raja, K. Passi, and A. Mahanti, "Optimization of 5g/6g Telecommunication Infrastructure Through an NFV-Based Element Management System," Symmetry, vol. 14, no. 5, p. 978, 2022, doi: 10.3390/sym14050978.
[12] M. Kist, J. F. Santos, D. Collins, J. Rochol, L. A. DaSilva, and C. B. Both, "AIRTIME: End-to-End Virtualization Layer for RAN-as-a-Service in Future Multi-Service Mobile Networks," Ieee Transactions on Mobile Computing, vol. 21, no. 8, pp. 2701-2717, 2022, doi: 10.1109/tmc.2020.3046535.
[13] K. Kaâniche, S. Othmen, A. Alfahid, A. Yousef, M. Albekairi, and O. I. Elhamrawy, "Enhancing Service Availability and Resource Deployment in IoT Using a Shared Service Replication Method," Heliyon, vol. 10, no. 3, p. e25255, 2024, doi: 10.1016/j.heliyon.2024.e25255.
[14] N. Kabbara, A. Mwangi, M. Gibescu, A. Abedi, A. Ştefanov, and P. Pálenský, "Specifications of a Simulation Framework for Virtualized Intelligent Electronic Devices in Smart Grids Covering Networking and Security Requirements," 2023, doi: 10.1109/powertech55446.2023.10202950.
[15] L. Golightly, P. Modesti, and V. Chang, "Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator," Journal of Cybersecurity and Privacy, vol. 3, no. 3, pp. 464-492, 2023, doi: 10.3390/jcp3030024.
[16] N. Kabbara et al., "Towards Software-Defined Protection, Automation, and Control in Power Systems: Concepts, State of the Art, and Future Challenges," Energies, vol. 15, no. 24, p. 9362, 2022, doi: 10.3390/en15249362.
[17] Z. Jia, Q. Wu, C. Dong, C. Yuen, and Z. Han, "Column Generation for Optimization Problems in Communication Networks," Ieee Network, vol. 37, no. 3, pp. 86-92, 2023, doi: 10.1109/mnet.108.2100634.
[18] H. Wang and M. Rahman, "Intelligent resource allocation optimization for cloud computing via machine learning," arXiv Preprint, 2025.
[19] Y. Zhang and X. Wang, "Adaptive resource scheduling for edge-to-cloud deep learning systems via reinforcement learning," Future Generation Computer Systems, vol. 140, pp. 21-34, 2025.
[20] S. Saxena, "Clustering Based Prediction for VM Workload in Green Computation," Asian Journal of Convergence in Technology, vol. 10, no. 1, pp. 1-8, 2024, doi: 10.33130/ajct.2024v10i01.001.
[21] Y. Shen et al., "‘Cloud for Youth’: An Implementation Research of Cloud‐based Solutions for Bridging the Digital Divide in Rural China," British Journal of Educational Technology, 2025, doi: 10.1111/bjet.70037.
[22] A. S. Mahapatra, S. Sengupta, A. Dasgupta, B. Sarkar, and R. T. Goswami, "What is the impact of demand patterns on integrated online-offline and buy-online-pickup in-store (BOPS) retail in a smart supply chain mana gement?," Journal of Retailing and Consumer Services, vol. 82, p. 104093, 2025/1/1/ 2025, doi: 10.1016/J.JRETCONSER.2024.104093.
[23] G. S. Atencio and M. Umaña-Ramírez, "The Evolution and Trends of Hyperconvergence in the Telecommunications Sector: A Competitive Intelligence Review," Dyna, vol. 90, no. 227, pp. 126-132, 2023, doi: 10.15446/dyna.v90n227.107360.
[24] Y. Shi, P. Wang, X. Zhu, and H. Zhu, "Reconfigurable Digital Satellite-Borne Base Station Design and Virtual Function Fast Migration Algorithm," Sensors, vol. 23, no. 17, p. 7591, 2023, doi: 10.3390/s23177591.
Downloads
Publication Timeline
- Submitted
- Revised
- Accepted
Issue
Section
License
Copyright (c) 2025 Mohammad Alimorad, Babak Nouri Moghaddam (Author); Abbas Mirzaei (Corresponding author); Nasser Mikaeilvand, Azadeh Zamanifar (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.