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This paper addresses the re-engineering of congestion control for TCP applications over networks with coupled wireless links. Using queueing delay as a congestion measure, we show that optimal TCP congestion control can be achieved by developing window-control oriented implicit primal-dual solvers for intended network utility maximization problem. Capitalizing on such an idea, we prove the existence of scalable, easy-to-deploy, yet optimal end-to-end congestion control schemes for networks with wireless links, given that the wireless access point appropriately schedules packet transmissions. A class of so-called QUIC-TCP congestion control algorithms are developed. Relying on a Lyapunov method, we rigorously establish the global convergence/stability of the proposed QUIC-TCP to optimal equilibrium in the network fluid model. Numerical results corroborate the merits of the proposed schemes in IPv6-based Internet environments. INDEX TERMS Congestion control, wireless-link scheduling, convex optimization, network fluid model, Lyapunov method.

Understanding the network usage patterns of university users is very important today. This paper focuses on the research of DNS request behaviors of university users in Shanghai, China. Based on the DNS logs of a large number of university users recorded by CERNET, we conduct a general analysis of the behavior of network browsing from two perspectives: the characteristics of university users’ behavior and the market share of CDN service providers. We also undertake experiments on DNS requests patterns for CDN service providers using different prediction models. Firstly, in order to understand the university users’ Internet access patterns, we select the top seven universities with the most DNS requests and reveal the characteristics of different university users. Subsequently, to obtain the market share of different CDN service providers, we analyze the overall situation of the traffic distribution among different CDN service providers and its dynamic evolution trend. We find that Tencent Cloud and Alibaba Cloud are leading in both IPv4 and IPv6 traffic. Baidu Cloud has close to 15% in IPv4 traffic, but almost no fraction in IPv6 traffic. Finally, for the characteristics of different CDN service providers, we adopt statistical models, traditional machine learning models, and deep learning models to construct tools that can accurately predict the change in request volume of DNS requests. The conclusions obtained in this paper are beneficial for Internet service providers, CDN service providers, and users.

With the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. On weekends, traffic usage is smaller than on weekdays, but the distribution does not change much. We find that the self-similarity of IPv4 traffic is close to that of IPv6 traffic, and there is a strong positive correlation between IPv6 traffic and IPv4 traffic. Based on our findings on traffic patterns, we propose a new IPv6 traffic prediction model by combining the advantages of the statistical and deep learning models. In addition, our model would extract useful information from the corresponding IPv4 traffic to enhance the prediction. Based on two real-world datasets, it is shown that the proposed model outperforms eight baselines with a lower prediction error. In conclusion, our approach is helpful for network resource allocation and network management.