The heterogeneity of applications and their divergent resource requirements lead to uneven traffic distribution and imbalanced resource utilization across data center networks(DCNs).We propose a fine-grained baseband ...The heterogeneity of applications and their divergent resource requirements lead to uneven traffic distribution and imbalanced resource utilization across data center networks(DCNs).We propose a fine-grained baseband function reallocation scheme in heterogeneous optical switching-based DCNs.A deep reinforcement learning-based functional split and resource mapping approach(DRL-BFM)is proposed to maximize throughput in high-load server racks by implementing load balancing in DCNs.The results demonstrate that DRL-BFM improves the throughput by 20.8%,22.8%,and 29.8%on average compared to existing algorithms under different computational capacities,bandwidth constraints,and latency conditions,respectively.展开更多
基金supported by the National Key R&D Program of China(Nos.2023YFB2905500 and 2023YFB2805302)the National Natural Science Foundation of China(No.62205026)the Beijing Institute of Technology Research Fund Program for Young Scholars,and the Open Fund of IPOC(BUPT)。
文摘The heterogeneity of applications and their divergent resource requirements lead to uneven traffic distribution and imbalanced resource utilization across data center networks(DCNs).We propose a fine-grained baseband function reallocation scheme in heterogeneous optical switching-based DCNs.A deep reinforcement learning-based functional split and resource mapping approach(DRL-BFM)is proposed to maximize throughput in high-load server racks by implementing load balancing in DCNs.The results demonstrate that DRL-BFM improves the throughput by 20.8%,22.8%,and 29.8%on average compared to existing algorithms under different computational capacities,bandwidth constraints,and latency conditions,respectively.
