摘要
Virtual network embedding (VNE) is an essential part of network virtualization, which is considered as one of the most promising way for the future network. Its main object is to efficiently assign the nodes and links of a virtual network (VN) to a shared substrate network (SN), The NP-hard and exiting studies have put forward several heuristic algorithms. However, most of the algorithms only consider the local resource of nodes, such as CPU and bandwidth (BW), to decide the embedding, and ignore the significant impact of network attributes. Based on the attributes of entire network, a model of the connectivity between each pair of nodes was formulated to measure the resource ranking of the nodes, and a new two-stage embedding algorithm was proposed. Thereafter, the node mapping and link mapping can be jointly considered. Extensive simulation shows that the proposed algorithm improves the performance of VNE by increasing the revenue/cost ratio and acceptance ratio of VN requests while reducing the runtime.
Virtual network embedding (VNE) is an essential part of network virtualization, which is considered as one of the most promising way for the future network. Its main object is to efficiently assign the nodes and links of a virtual network (VN) to a shared substrate network (SN), The NP-hard and exiting studies have put forward several heuristic algorithms. However, most of the algorithms only consider the local resource of nodes, such as CPU and bandwidth (BW), to decide the embedding, and ignore the significant impact of network attributes. Based on the attributes of entire network, a model of the connectivity between each pair of nodes was formulated to measure the resource ranking of the nodes, and a new two-stage embedding algorithm was proposed. Thereafter, the node mapping and link mapping can be jointly considered. Extensive simulation shows that the proposed algorithm improves the performance of VNE by increasing the revenue/cost ratio and acceptance ratio of VN requests while reducing the runtime.
基金
supported by the National Basic Research Program of China (2012CB315801)
the National Natural Science Foundation of China (61302089)
the fundamental research funds for the Central Universities (2013RC0113)