An Improved Link Model for Window Flow Control and Its Application to FAST TCP

This paper presents a link model which captures the queue dynamics in response to a change in a transmission control protocol (TCP) source's congestion window. By considering both self-clocking and the link integrator effect, the model generalizes existing models and is shown to be more accurate by both open loop and closed loop packet level simulations. It reduces to the known static link model when flows' round trip delays are identical, and approximates the standard integrator link model when there is significant cross traffic. We apply this model to the stability analysis of fast active queue management scalable TCP (FAST TCP) including its filter dynamics. Under this model, the FAST control law is linearly stable for a single bottleneck link with an arbitrary distribution of round trip delays. This result resolves the notable discrepancy between empirical observations and previous theoretical predictions. The analysis highlights the critical role of self-clocking in TCP stability, and the proof technique is new and less conservative than existing ones

Throughput analysis of Scalable TCP congestion control

Scalable TCP (STCP) has been proposed a congestion control algorithm for high speed networks. We present a mathematical analysis of STCP´s congestion window through the slow start and the congestion avoidance phases. We analyse the evolution of congestion windows for single and multiple flows and for DropTail queues with and without random loss. We derive throughput formulas for the different setups and reveal the inherent unfairness between different round trip times flows. Our mathematical analysis is compared to state-of-the-art network simulator (ns) results, which verifies our model´s accuracy. With our analysis we want to adaptively control STCP´s fixed parameters in order to overcome the fairness problems. These experiments are work in progress and will be presented in a sequel paper

Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments

Congestion-Aware Scalable Video Streaming

Postprin

Effective video multicast over wireless internet

With the rapid growth of wireless networks and great success of Internet video, wireless video services are expected to be widely deployed in the near future. As different types of wireless networks are converging into all IP networks, i.e., the Internet, it is important to study video delivery over the wireless Internet. This paper proposes a novel end-system based adaptation protocol calledWireless Hybrid Adaptation Layered Multicast (WHALM) protocol for layered video multicast over wireless Internet. In WHALM the sender dynamically collects bandwidth distribution from the receivers and uses an optimal layer rate allocation mechanism to reduce the mismatches between the coarse-grained layer subscription levels and the heterogeneous and dynamic rate requirements from the receivers, thus maximizing the degree of satisfaction of all the receivers in a multicast session. Based on sampling theory and theory of probability, we reduce the required number of bandwidth feedbacks to a reasonable degree and use a scalable feedback mechanism to control the feedback process practically. WHALM is also tuned to perform well in wireless networks by integrating an end-to-end loss differentiation algorithm (LDA) to differentiate error losses from congestion losses at the receiver side. With a series of simulation experiments over NS platform, WHALM has been proved to be able to greatly improve the degree of satisfaction of all the receivers while avoiding congestion collapse on the wireless Internet