Network virtualization is considered as a powerful way to deploy different network architectures and protocols over a shared physical infrastructure.Virtual network(VN)embedding plays an important role in the network virtualization environment.Virtual network embedding is mainly about mapping virtual nodes and virtual links onto the physical network resources efficiently.However,load balancing of substrate network has not received much consideration.This paper proposes an adaptive virtual network mapping algorithm based on status feedback.The main contribution is to embed the virtual network requests according to the current load distribution of substrate network.This adaptive algorithm differentiates the residual bandwidth of substrate links and takes full advantage of the multi-path to improve the load balancing of the substrate network.Simulation experiments show that the proposed algorithm improves the load distribution of substrate network distinctly while reducing the average substrate link stress significantly. Network virtualization is considered as a powerful way to deploy different network architectures and protocols over a shared physical infrastructure. Virtual Network (VN) embedding plays an important role in the network virtualization environment. Virtual network embedding is mainly about mapping virtual nodes and virtual links onto the physical network resources efficiently.However, load balancing of substrate network has not received much consideration.This paper proposes an adaptive virtual network mapping algorithm based on status feedback. The main contribution is to embed the virtual network requests according to the current load distribution of substrate network. This adaptive algorithm differentiates the residual bandwidth of substrate links and takes full advantage of the multi-path to improve the load balancing of the substrate network. Simulation experiments show that the proposed algorithm improves the load distribution of substrate network distinctly while reducing the average sub strate link stress significantly.