Software-defined networking (SDN) has emerged as a promising approach to enable network innovation, which can provide network virtualization through a hypervisor plane to share the same cloud datacenter network among multiple virtual networks. While, this attractive approach may bring some new problem that leads to more susceptible to the failure of network component because of the separated control and forwarding planes. The centralized control and virtual network sharing the same physical network are becoming fragile and prone to failure if the topology of virtual network and the control path is not properly designed. Thus, how to map virtual network into physical datacenter network in virtualized SDN while guaranteeing the survivability against the failure of physical component is extremely important and should fully consider more influence factors on the survivability of virtual network. In this paper, combining VN with SDN, a topology-aware survivable virtual network embedding approach is proposed to improve the survivability of virtual network by an enhanced virtual controller embedding strategy to optimize the placement selection of virtual network without using any backup resources. The strategy explicitly takes account of the network delay and the number of disjoint path between virtual controller and virtual switch to minimize the expected percentage of control path loss with survivable factor. Extensive experimental evaluations have been conducted and the results verify that the proposed technology has improved the survivability and network delay while keeping the other within reasonable bounds.

In recent years, deep learning based image recognition, speech recognition, text translation and other related applications have brought great convenience to people's lives. With the advent of the era of internet of everything, how to run a computationally intensive deep learning algorithm on a limited resources edge device is a major challenge. For an edge oriented computing vector processor, combined with a specific neural network model, a new data layout method for putting the input feature maps in DDR, rearrangement of the convolutional kernel parameters in the nuclear memory bank is proposed. Aiming at the difficulty of parallelism of two-dimensional matrix convolution, a method of parallelizing the matrix convolution calculation in the third dimension is proposed, by setting the vector register with zero as the initial value of the max pooling to fuse the rectified linear unit (ReLU) activation function and pooling operations to reduce the repeated access to intermediate data. On the basis of single core implementation, a multi-core implementation scheme of Inception structure is proposed. Finally, based on the proposed vectorization method, we realize five kinds of neural network models, namely, AlexNet, VGG16, VGG19, GoogLeNet, ResNet18, and performance statistics and analysis based on CPU, gtx1080TI and FT2000 are presented. Experimental results show that the vector processor has better computing advantages than CPU and GPU, and can calculate large-scale neural network model in real time.