Machine Learning-Assisted Lag Prediction and Adaptive Network Optimization for Competitive Multiplayer Online Gaming

Abstract

The rapid growth of competitive multiplayer online gaming has increased the demand for reliable and low-latency network communication. Modern online games require real-time data exchange between clients and servers, making them highly sensitive to network disruptions such as latency, packet loss, and jitter. Traditional Quality of Service (QoS) mechanisms typically operate through predefined rules and reactive traffic management strategies that may fail to address dynamic network congestion. This paper proposes a Machine Learning-Assisted Lag Prediction and Adaptive Network Optimization Framework for competitive multiplayer online gaming environments. The proposed framework combines machine learning-based latency forecasting with adaptive network resource allocation to proactively mitigate network degradation before gameplay is affected. By analysing real-time network metrics, the system predicts potential lag events and dynamically adjusts traffic prioritisation mechanisms. The proposed architecture aims to improve gaming performance, reduce latency spikes, and enhance overall Quality of Experience (QoE) for players. Additionally, the study explores the application of emerging networking technologies such as Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) to further optimise gaming network performance.