AN EXPERIMENTAL COMPARISON OF PUBLIC INTERNET VS. PRIVATE DIRECT CONNECT FOR ENTERPRISE CLOUD PERFORMANCE
Department of Computer Science and Engineering AKS University, Satna (M.P.), India
The decision to migrate latency-sensitive workloads to the cloud has made the issue of whether to use public internet-based transit or direct interconnects a key architectural choice. In this paper, an empirical performance comparison has been described between traditional Public Internet (with IPsec VPN) and Private Direct Connect (AWS Direct Connect) to measure the reliability and speed trade-offs. We evaluated throughput, Round-Trip Time (RTT), and packet loss with a sustained 14-day cycle in a distributed experimental infrastructure in two large metropolitan areas. As our experimental results show, although the public internet has adequate bandwidth to support an asynchronous workload, it exhibits considerable micro-burst latency peaks, and jitter values reach as high as 35ms during periods of extreme congestion. Conversely, the Private Direct Connect continued to record sub-5ms jitter variance and a 22-percent greater sustained goodput on large-scale database synchronisation. In addition, we examine the effects of protocol overhead and show that the encryption layer of public VPNs decreases effective payload capacity by nearly 8%. Such results provide a strict, data-driven benchmark against which enterprise architects can measure the cost-performance ratio of dedicated cloud circuits, in the case of real-time analytics and high-frequency financial applications.
Verma, S. & Shrivastava, P. (2026). An Experimental Comparison of Public Internet vs. Private Direct Connect for Enterprise Cloud Performance. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.396
Verma, Saurabh, and Pragya Shrivastava. "An Experimental Comparison of Public Internet vs. Private Direct Connect for Enterprise Cloud Performance." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.396.
Verma, Saurabh, and Pragya Shrivastava. "An Experimental Comparison of Public Internet vs. Private Direct Connect for Enterprise Cloud Performance." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.396.
2.Bose, R., Fadaei, S., Mohan, N., Kassem, M., Sastry, N. and Ott, J., (2024), November. It's a bird? it's a plane? It's CDN!: investigating content delivery networks in the LEO satellite networks era. In Proceedings of the 23rd ACM Workshop on Hot Topics in Networks (pp. 1-9).
3.da Silva Pinto, A., (2025). SIMD-Optimized Indexing for Columnar Databases: Benchmarking Performance in Real-Time Analytical Workloads (Master's thesis, Universidade do Porto (Portugal)).
4.Edgar, M., (2024). Time to First Byte (TTFB). In Speed Metrics Guide: Choosing the Right Metrics to Use When Evaluating Websites (pp. 19-34). Berkeley, CA: Apress.
5.Katal, A., Dahiya, S. and Choudhury, T., (2023). Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing, 26(3), pp.1845-1875.
6.Khan, A., Umar, A.I., Shirazi, S.H., Ishaq, W., Shah, M., Assam, M. and Mohamed, A., (2022). QoS-aware cost minimization strategy for AMI applications in smart grid using cloud computing. Sensors, 22(13), p.4969.
7.Lorincz, J., Klarin, Z. and Ožegović, J., (2021). A comprehensive overview of TCP congestion control in 5G networks: Research challenges and future perspectives. Sensors, 21(13), p.4510.
8.Maheshwari, H., (2025). Data-driven machine learning for simulating and predicting urban intersection traffic (Doctoral dissertation).
9.Nyakomitta, P.S. and Abeka, S.O., (2020). Security investigation on remote access methods of virtual private network. Global journal of computer science and technology, 20(1), pp.1-10.
10.Parmar, Y., Kumar, S., Karthikeyan, V., Shukla, G. and Mishra, D., (2025), March. Challenges and Solutions for Securing Cloud-Based Virtual Private Networks (VPNs). In 2025 International Conference on Automation and Computation (AUTOCOM) (pp. 655-660). IEEE.