Cluster-Based Traffic Optimization and Dynamic Resource Allocation for Network Systems

As the types of services in network communication systems become increasingly complex, traditional static resource allocation mechanisms often exhibit problems such as slow response, resource waste, and system bottlenecks when facing dynamic traffic loads. To address the above challenges, this paper proposes a network traffic optimization method based on K-Means clustering combined with dynamic resource scheduling. This method automatically identifies and classifies different types of services by performing unsupervised clustering of traffic characteristics, and dynamically adjusts the allocation strategy of bandwidth and computing resources accordingly, thereby improving the overall efficiency and stability of the network system. The experiment simulates a variety of typical load scenarios and compares static allocation with traditional priority scheduling strategies. The results show that the proposed method has superior performance in multiple performance indicators such as average delay reduction, resource utilization improvement, and system throughput enhancement, especially under medium and high load conditions. The research results show that this method has good adaptability and engineering deployment potential, and is suitable for real-time resource optimization tasks in large-scale heterogeneous network environments. This work provides an effective technical path for data-driven network scheduling mechanisms and lays a theoretical foundation for the construction of intelligent network resource management systems.