What question did this study set out to answer?

The central aim is to investigate how machine learning can enhance Quality of Service in enterprise networks.

April 12, 2026Open Access

ML-Based QoS Optimization In Enterprise Networks

Key Points

The central aim is to investigate how machine learning can enhance Quality of Service in enterprise networks.
Review of existing literature on ML applications for QoS optimization.
Analysis of ML paradigms including supervised learning for traffic classification and unsupervised learning for anomaly detection.
Examination of integration strategies for ML in Software-Defined Networking (SDN) frameworks.
ML-based QoS optimization enables proactive management of network resources.
Deep Learning models effectively identify application requirements without deep packet inspection.
Identifying critical challenges such as model interpretability and real-time inference at the network edge.

Abstract

The digital infrastructure of the modern enterprise is undergoing a radical transformation, driven by the widespread adoption of cloud-native applications, real-time collaboration tools, and high-bandwidth multimedia services. In this dynamic landscape, traditional Quality of Service (QoS) mechanisms, which rely on static priority queuing and manually defined traffic classes, are increasingly incapable of managing the volatility of network demand. This review explores the paradigm shift toward Machine Learning (ML)-based QoS optimization. By transitioning from reactive, threshold-based management to proactive, intent-driven architectures, ML enables enterprise networks to achieve \\\"Cognitive Traffic Engineering.\\\" This article examines how various ML paradigms—including supervised learning for traffic classification, unsupervised learning for anomaly detection, and reinforcement learning for dynamic resource allocation—can be synthesized into a unified optimization fabric. We analyze the efficacy of Deep Learning models, such as Convolutional Neural Networks and Long Short-Term Memory units, in identifying application-layer requirements within encrypted tunnels without the need for Deep Packet Inspection. Furthermore, the review addresses the architectural integration of ML within Software-Defined Networking (SDN) and SD-WAN frameworks, enabling the \\\"Self-Driving Network\\\" vision. Critical challenges, such as model interpretability, real-time inference latency at the network edge, and data drift in multi-tenant environments, are discussed in depth. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building resilient, high-performance enterprise networks. The findings suggest that ML-driven QoS is the foundational technology required to satisfy the stringent Service Level Agreements of the modern digital enterprise, ensuring that network resources are distributed with machine-speed precision and contextual intelligence.

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