In this paper, we used the data set of domain names Global Top 1M provided by Alexa to analyze the effectiveness of Fallback in ECN. For the same test server, we first negotiate a connection with Not-ECN-Capable, and then negotiate a connection with ECN-Capable, if the sender does not receive the response to ECN-Capable negotiation from the receiver by the end of retransmission timeout, it will enter the Fallback state, and switch to negotiating a connection with Not-ECN-Capable. By extracting the header fields of the TCP/IP packets, we confirmed that in most regions, connectivity will be slightly improved after Fallback is enabled and Fallback has a positive effect on the total time of the whole access process. Meanwhile, we provided the updated information about the characteristics related to ECN with Fallback in different regions by considering the geographical region distribution of all targeted servers.

ECN, as a decisive approach for TCP congestion control, has been proposed for many years. However, its deployment on the Internet is much slower than expected. In this paper, we investigate the state of the deployment of ECN (Explicit Congestion Notification) on the Internet from a different viewpoint. We use the data set of web domains published by Alexa as the hosts to be tested. We negotiate an ECN-Capable and a Not ECN-Capable connections with each host and collect all packets belonging to the connections. By analyzing the header fields of the TCP/IP packets, we dig out the deployment rate, connectivity, variation of round-trip time and time to live between the Not ECN-Capable and ECN-Capable connections as well as the rate of IPv6-Capable web servers. Especially, it is clear that the connectivity is different from the domains (regions on the Internet). We hope that the findings acquired from this study would incentivize ISPs and administrators to enable ECN in their network systems.

The accurate and fast estimation of link price is the key component of network-based congestion control schemes. A fast estimation method A2DPM is presented. Multiple hashes on IP identifier of packet header are adopted to accelerate the side information transmission, so accurate estimation of maximum price on the flow forwarding path can be realized after the receipt of just a few probe packets, and the sender is capable of reacting to congestion more quickly, making it suitable to meet the demands of dynamic networks.

This paper introduces a network-aware video delivery framework where the quality-of-service (QoS) interaction between prioritized packet video and relative differentiated service (DiffServ) network is taken into account. With this framework, we propose a dynamic class mapping (DCM) scheme to allow video applications to cope with service degradation and class-based resource constraint in a time-varying network environment. In the proposed scheme, an explicit congestion notification (ECN)-based feedback mechanism is utilized to notify the status of network classes and the received service quality assessment to the end-host applications urgently. Based on the feedback information, DCM agent at ingress point can dynamically re-map each packet onto a network class in order to satisfy the desired QoS requirement. Simulation results verify the enhanced QoS performance of the streaming video application by comparing the static class-mapping and the class re-mapping based on loss-driven feedback.

The window flow control based end-to-end TCP congestion control may cause unfair resource allocation among multiple TCP connections with different RTTs (round trip times) at a bottleneck link. In this paper, in order to improve this unfairness, we propose the active ECN which is an ECN based active queue mechanism (AQM). A bottleneck router with the proposed mechanism marks TCP segments with a probability which depends on the RTT of each connection. By enabling the TCP senders to reduce their transmission rate when their packets are marked, the proposed mechanism can realize the same transmission rate among TCP connections with different RTTs. Furthermore, the active ECN can directly mark ACKs from TCP receivers, while the conventional ECN marks TCP segments coming from the TCP senders. As a result, the queue length distribution at the bottleneck link gets stabilized, because the sender can quickly react to the marking according to variation of the queue length.

Explicit Congestion Notification (ECN) supports the binary congestion information of the network for adjusting the window size. However, this results in the oscillation of the window size and the queue length due to the insufficient congestion information. In this paper, we propose the window-based congestion control mechanism with the modified ECN mechanism. The proposed scheme is based on extracting the network status from the consecutive binary congestion information provided by ECN. From the explicit network information, we estimate the allowable window size to achieve better performance. Through the simulations, the effectiveness of the proposed algorithm is shown as compared with the ECN algorithm.