An Optimal Algorithm for Traffic Scheduling in SRv6 Network Based on Deep Learning
Current traffic scheduling methods in SRv6 network are mainly based on fixed or heuristic rules,which lack of the ability to schedule overall network traffic flexibly and are difficult to adapt to dynamic network environment changes.To address the deficiency in key flow identification within SRv6 network,the article introduced a key flow identification algorithm based on deep reinforcement learning.This approach established a key flow learning model adapted to the dynamic changes of the network,identifying sets of key flows that significantly impact network performance across various traffic matrices.In response to the challenges of traffic scheduling in SRv6 network,the article developed an optimization algorithm for traffic scheduling,rooted in key flow analysis.This algorithm employed linear programming to determine the optimal explicit path for each key flow and utilized different routing methods for ordinary flows and key flows,effectively enhancing network performance.The experimental results demonstrate that the proposed traffic scheduling algorithm leads to a significant improvement in network load balancing and a substantial reduction in network end-to-end transmission delay.
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维普
