Software-Defined Network architecture offers network virtualization through a hypervisor plane to share the same physical substrate among multiple virtual networks. However, for this hypervisor plane, how to map ...Software-Defined Network architecture offers network virtualization through a hypervisor plane to share the same physical substrate among multiple virtual networks. However, for this hypervisor plane, how to map a virtual network to the physical substrate while guaranteeing the survivability in the event of failures, is extremely important. In this paper, we present an efficient virtual network mapping approach using optimal backup topology to survive a single link failure with less resource consumption. Firstly, according to whether the path splitting is supported by virtual networks, we propose the OBT-I and OBT-II algorithms respectively to generate an optimal backup topology which minimizes the total amount of bandwidth constraints. Secondly, we propose a Virtual Network Mapping algorithm with coordinated Primary and Backup Topology （VNM-PBT） to make the best of the substrate network resource. The simulation experiments show that our proposed approach can reduce the average resource consumption and execution time cost, while improving the request acceptance ratio of VNs.展开更多
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This research was sponsored by the National Basic Research Program （973 program） of China （2012CB315901, 2013C8329104）, the National Natural Science Foundation of China （61372121, 61309020）, and the National High-Tech Research and Development Program （863 Program） of Chi- na （2011AA01A103, 201 1AA01A101, 2013AA013505）.
文摘Software-Defined Network architecture offers network virtualization through a hypervisor plane to share the same physical substrate among multiple virtual networks. However, for this hypervisor plane, how to map a virtual network to the physical substrate while guaranteeing the survivability in the event of failures, is extremely important. In this paper, we present an efficient virtual network mapping approach using optimal backup topology to survive a single link failure with less resource consumption. Firstly, according to whether the path splitting is supported by virtual networks, we propose the OBT-I and OBT-II algorithms respectively to generate an optimal backup topology which minimizes the total amount of bandwidth constraints. Secondly, we propose a Virtual Network Mapping algorithm with coordinated Primary and Backup Topology （VNM-PBT） to make the best of the substrate network resource. The simulation experiments show that our proposed approach can reduce the average resource consumption and execution time cost, while improving the request acceptance ratio of VNs.
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.
