TCP Congestion Control Identification

Transmission Control Protocol (TCP) carries most of the traffic on the Internet these days. There are several implementations of TCP, and the most important difference among them is their mechanism for controlling congestion. One of the methods for determining type of a TCP is active probing. Active probing considers a TCP implementation as a black box, sends different streams of data to the appropriate host. According to the response received from the host, it figures out the type of TCP version implemented. TCP Behavior Inference Tool (TBIT) is an implemented tool that uses active probing to check the running TCP on web servers. It can check several aspects of the running TCP including initial value of congestion window, congestion control algorithm, conformant congestion control, response to selective acknowledgment, response to Explicit Congestion Notification (ECN) and time wait duration. In this paper we focus on congestion control algorithm aspect of it, explain the mechanism used by TBIT and present the results

A First Look at QUIC in the Wild

For the first time since the establishment of TCP and UDP, the Internet transport layer is subject to a major change by the introduction of QUIC. Initiated by Google in 2012, QUIC provides a reliable, connection-oriented low-latency and fully encrypted transport. In this paper, we provide the first broad assessment of QUIC usage in the wild. We monitor the entire IPv4 address space since August 2016 and about 46% of the DNS namespace to detected QUIC-capable infrastructures. Our scans show that the number of QUIC-capable IPs has more than tripled since then to over 617.59 K. We find around 161K domains hosted on QUIC-enabled infrastructure, but only 15K of them present valid certificates over QUIC. Second, we analyze one year of traffic traces provided by MAWI, one day of a major European tier-1 ISP and from a large IXP to understand the dominance of QUIC in the Internet traffic mix. We find QUIC to account for 2.6% to 9.1% of the current Internet traffic, depending on the vantage point. This share is dominated by Google pushing up to 42.1% of its traffic via QUIC

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

A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks

HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for video delivery in mobile networks. Over the past few years, several HAS algorithms have been introduced in order to improve user quality-of-experience (QoE) by bit-rate adaptation. Their difference is mainly the required input information, ranging from network characteristics to application-layer parameters such as the playback buffer. Interestingly, despite the recent outburst in scientific papers on the topic, a comprehensive comparative study of the main algorithm classes is still missing. In this paper we provide such comparison by evaluating the performance of the state-of-the-art HAS algorithms per class, based on data from field measurements. We provide a systematic study of the main QoE factors and the impact of the target buffer level. We conclude that this target buffer level is a critical classifier for the studied HAS algorithms. While buffer-based algorithms show superior QoE in most of the cases, their performance may differ at the low target buffer levels of live streaming services. Overall, we believe that our findings provide valuable insight for the design and choice of HAS algorithms according to networks conditions and service requirements.Comment: 6 page

User-space Multipath UDP in Mosh

In many network topologies, hosts have multiple IP addresses, and may choose among multiple network paths by selecting the source and destination addresses of the packets that they send. This can happen with multihomed hosts (hosts connected to multiple networks), or in multihomed networks using source-specific routing. A number of efforts have been made to dynamically choose between multiple addresses in order to improve the reliability or the performance of network applications, at the network layer, as in Shim6, or at the transport layer, as in MPTCP. In this paper, we describe our experience of implementing dynamic address selection at the application layer within the Mobile Shell. While our work is specific to Mosh, we hope that it is generic enough to serve as a basis for designing UDP-based multipath applications or even more general APIs