An Analytical Model for Enhancing Scalability and Reliability in Computer Networks

Abstract

Computer networks, as the vital backbone of modern data communication, play an undeniable role in ensuring the stability and quality of digital services. With the rapid growth of emerging technologies such as the Internet of Things (IoT), Cloud Computing, and Blockchain Networks, the demand for network infrastructures capable of handling massive traffic volumes and dynamic node expansions without significant performance degradation has become more pressing than ever. Two critical factors in this regard—Scalability and Reliability—directly influence not only network performance but also maintenance costs, Quality of Service (QoS), and overall user experience. While previous studies have often focused on either scalability or reliability in isolation, the absence of a comprehensive analytical model addressing both dimensions simultaneously remains a major research gap.

This study proposes a novel analytical model aimed at improving both scalability and reliability in computer networks. The research follows an analytical–applied methodology, combining simulation (using NS-3 and Packet Tracer) with real-world data analysis from an enterprise network comprising over 1,200 active users. Several Key Performance Indicators (KPIs) were defined to evaluate the model, including Availability Rate, End-to-End Latency (E2E), Connection Failure Rate, Congestion Rate, and a Relative Scalability Index.Simulation results demonstrate that the proposed model achieved a 34.7% improvement in scalability when the number of nodes increased from 500 to 2000 compared with baseline architectures. Under heavy load conditions, the average E2E latency decreased from 320 ms to 240 ms, reflecting a 25% reduction in response time. Furthermore, the connection failure rate dropped significantly, from 7.8% in traditional models to 3.1% in the proposed approach, while the overall availability rate improved from 92.1% to 97.4%. These improvements were not only statistically significant (p-value < 0.05) but also descriptively meaningful, highlighting the role of redundancy mechanisms and load balancing algorithms in reducing network bottlenecks and improving stability.

From an economic perspective, the model also reduced operational downtime costs by approximately 18% over a six-month period, while end-user satisfaction, measured through a survey of 320 participants, increased from 71% to 86%. These results emphasize that the proposed approach provides benefits beyond technical performance, offering practical and economic advantages as well.Overall, this research demonstrates that a comprehensive analytical model can simultaneously address the dual challenges of scalability and reliability in modern networks. The findings pave the way for the development of intelligent network management systems, particularly in the context of 5G/6G networks and large-scale IoT environments. Future work is recommended to integrate the proposed model with Machine Learning algorithms and Self-Organizing Network (SON) architectures for further intelligent performance optimization.