In scenarios such as distributed collaborative training of AI large models in cloud data center networks, different types of data flows have differentiated Quality of Service (QoS) requirements for the network. To meet these differentiated requirements, it is necessary to first identify the types of data flows. Currently, there are two major challenges: one is the classification of encrypted flows. Traditional methods based on ports and DPI (Deep Packet Inspection) will fail, and intelligent recognition methods using machine learning need to be adopted. The second is the challenge of online real-time classification. Most existing machine learning-based traffic classification methods consume a lot of resources and have a slow speed, are limited to offline processing, and are difficult to cope with online real-time processing. Therefore, this paper proposes a lightweight encrypted traffic classification method based on deep learning. This method designs a lightweight multi-scale feature fusion module (Lightweight Multi-scale Atrous Spatial Pyramid Pooling, LM-ASPP), which enhances multi-scale feature extraction capabilities by using dilated convolution and depthwise separable convolution techniques while reducing computational overhead. Based on ShuffleNetV2, a lightweight encrypted traffic classification model integrating LM-ASPP and ShuffleNetV2 is designed. Experimental results show that this model achieves high accuracy while significantly reducing resource consumption and inference latency, achieving a balance between performance and resource consumption.

Computing power network (CPN) has been proposed to allocate and schedule computing power resources among cloud, network, and edge according to the needs of computing services. CPN can improve the utilization rate of various computing resource pools. However, it brings other challenges that how to transfer data packets based on computing resource information. Since the size of routing table will be too large to store and search with lots of computing information. To solve this problem, we define three computing service types firstly. Then propose a hierarchical routing mechanism for computing services in CPN. Based on this mechanism, CPN can improve data forwarding efficiency and user experience. In the future, we will research standard of computing resource identification to provide more intelligent service for various application.

Computing power network (CPN) has been proposed to allocate and schedule computing power resources among cloud, network, and edge according to the needs of computing services. CPN can improve the utilization rate of various computing resource pools. However, it brings another challenge that how to ensure the security of multi-tenant information and the resource information which they rent. To solve this problem, we propose an isolation architecture in CPN, add a tenant mapping management module in network control layer firstly. Then we design the security mapping process between the tenant and the computing routing node based on this architecture. At last, we propose a mapping method between tenants and computing routing nodes based on hash ring which can avoid the problem of data migration caused by increasing the number of computing routing nodes. In the future, we will study the mapping algorithm to improve the efficiency of CPN.

Cloud computing is a unified management and scheduling model of computing resources. To satisfy multiple resource requirements for various application, edge computing has been proposed. One challenge of edge computing is cross-domain data security sharing problem. Ciphertext policy attribute-based encryption (CP-ABE) is an effective way to ensure data security sharing. However, many existing schemes focus on could computing, and do not consider the features of edge computing. In order to address this issue, we propose a cross-domain data security sharing approach for edge computing based on CP-ABE. Besides data user attributes, we also consider access control from edge nodes to user data. Our scheme first calculates public-secret key peer of each edge node based on its attributes, and then uses it to encrypt secret key of data ciphertext to ensure data security. In addition, our scheme can add non-user access control attributes such as time, location, frequency according to the different demands. In this paper we take time as example. Finally, the simulation experiments and analysis exhibit the feasibility and effectiveness of our approach.

Edge computing has been proposed to satisfy delay requirement for various applications. It brings another challenge that the collaboration problem among cloud computing, edge computing and network resources. Diversification of computing power nodes makes the optimization of resource utilization more complicated. To solve this problem, we first describe a general computing power network (CPN) framework and analyze different roles and their focuses on computing power resource. Then we propose a computing power resource modeling approach from the operator's perspective. In this paper, we quantify the computing resource, storage resource and transmission time of wireless and wired. At last, we present completion time of a calculation task to measure computing power for CPN.

Software-defined networking (SDN) uses a centralized control plane to manage the whole network. Distributed SDN architecture has been proposed to resolve scalability and reliability problem. One challenge of multiple controllers is load balancing problem, that uneven load distribution among controllers. Dynamic switches migration is an effective way to solve this problem. However, the existing schemes focus on effective migration algorithms and different metrics to realize the load balancing of control plane in SDN, and do not consider the communication cost between controllers and computation cost of migration scheme. In order to address this issue, we propose a load balancing approach based on sharing data store. Sharing data store has the information of all controllers, and the calculator module in it can compute switches migration scheme based saved information. It can also reduce the computation and communication pressure of controllers. Simulation experiments exhibit the feasibility and effectiveness of our approach.