Segment Routing (SR) is a source routing paradigm which is widely used in Traffic Engineering (TE). By using SR, a node steers a packet through an ordered list of instructions called segments. By some extensions of interior gateway protocol, SR can be applied to IP/MPLS or IPv6 network without signal protocol. SR over IPv6 (SRv6) is attracting wide attention because of its interoperation ability with IPv6. However, upgrading the existing IPv6 network directly to a full SRv6 one can be difficult, because large-scale equipment replacement or software upgrade may cause economic and technical problems. TE in partially deployed SR network is becoming a hot research topic. In this paper, we propose the TE algorithm Weight Adjustment-SRTE (WA-SRTE) in partially deployed SRv6 network, in which SRv6 capable nodes are dispersedly deployed. Our objective is to minimize the network's maximum link utilization. WA-SRTE converts the TE problem into a Deep Reinforcement Learning problem and optimizes the OSPF weight, SRv6 node deployment and traffic paths simultaneously. Besides, traffic variation is also considered and we use a representative Traffic Matrix (TM) to epitomize the traffic characteristics over a period of time. Experiments demonstrate that with 20% to 40% of the SRv6 nodes deployed, we can achieve TE performance as good as in a full SR network for the experiment topologies. The results with WA remarkably outperform the results without it. Our algorithm also gets near-optimal results with changing traffic.

Network traffic classification is the foundation for many network security and network management applications. Recently, to preserve the privacy of the data which are generated in the mobile ends, federated learning (FL)-based classification methods are being proposed. Unfortunately, the performance of FL-based methods can seriously degrade when the client data have skewness. This is particularly true for mobile network traffic classification where the environments in the mobile ends are highly heterogeneous. In this article, we first conduct a measurement study on traffic classification accuracy through FL using real-world network traffic trace and we observe serious accuracy degradation due to heterogeneous environments. We propose a novel federated analytics (FA) approach, FEAT, to improve the accuracy. Note that FL emphasizes on model training, yet our FA performs local analytic tasks that can estimate traffic data skewness and select appropriate clients for FL model training. Our analytics tasks are performed locally and in a federated manner; thus, we preserve privacy as well. Our approach has strong theoretical properties where we exploit Hoeffding inequality to infer traffic data skewness and we leverage the Thompson Sampling for client selection. We evaluate our approach through extensive experiments using real-world traffic data sets QUIC and ISCX. The extensive experiments demonstrate that FEAT can improve traffic classification accuracy in heterogeneous environments.

Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in distributed TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal in a hybrid SDN scenario, we construct a reasonable virtual environment to meet different routing constraints brought by legacy routers and SDN switches for training the routing agents. To train the routing agents for determining the local routing policies according to local network observations, we introduce the difference reward assignment mechanism for encouraging agents to cooperatively take optimal routing action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen.