Abstract: This paper outlines a comprehensive AI-driven Quality of Service (QoS) optimization method, presenting a rigorous examination of its effectiveness through extensive experimentation and analysis. By applying real-world datasets to simulate network environments, the study systematically evaluates the proposed method’s impact across various QoS metrics. Key findings reveal substantial enhancements in reducing average latency, minimizing packet loss, and boosting bandwidth utilization compared to baseline scenarios, with the Deep Deterministic Policy Gradient (DDPG) model showcasing the most notable improvements. The research demonstrates that AI optimization strategies, particularly those leveraging DQN and DDPG algorithms, significantly improve upon conventional methods. Specifically, post-migration optimizations lead to a recovery and even surpassing of pre-migration QoS levels, with delays dropping to levels below initial readings, packet loss nearly eliminated, and bandwidth utilization markedly improved. The study further illustrates that while lower learning rates necessitate longer convergence times, they ultimately facilitate superior model performance and stability. In-depth case studies within a cloud data center setting underscore the system’s proficiency in handling large-scale Virtual Machine (VM) migrations with minimal disruption to network performance. The AI-driven optimization successfully mitigates the typical latency spikes, packet loss increases, and resource utilization dips associated with VM migrations, thereby affirming its practical value in maintaining high network efficiency and stability during such operations. Comparative analyses against traditional traffic engineering methods, rule-based controls, and other machine learning approaches consistently place the AI optimization method ahead, achieving up to an 8% increase in throughput alongside a 2 ms decrease in latency. Furthermore, the technique excels in reducing packet loss by 25% and elevating resource utilization rates, underscoring its prowess in enhancing network efficiency and stability. Robustness and scalability assessments validate the method’s applicability across diverse network scales, traffic patterns, and congestion levels, confirming its adaptability and effectiveness in a wide array of operational contexts. Overall, the research conclusively evidences the AI-driven QoS optimization system’s capacity to tangibly enhance network performance, positioning it as a highly efficacious solution for contemporary networking challenges.

Zhenhua Yang, Qiwen Yang and Minghong Yang. “Quality of Service-Oriented Data Optimization in Networks using Artificial Intelligence Techniques”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150691

@article{Yang2024,
title = {Quality of Service-Oriented Data Optimization in Networks using Artificial Intelligence Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150691},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150691},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Zhenhua Yang and Qiwen Yang and Minghong Yang}
}