Characterizing Bufferbloat and its Impact at End-hosts

Part 2: Performance at the EdgeInternational audienceWhile buffers on forwarding devices are required to handle bursty Internet traffic, overly large or badly sized buffers can interact with TCP in undesirable ways. This phenomenon is well understood and is often called “bufferbloat”. Although a number of previous studies have shown that buffering (particularly, in home) can delay packets by as much as a few seconds in the worst case, there is less empirical evidence of tangible impacts on end-users. In this paper, we develop a modified algorithm that can detect bufferbloat at individual end-hosts based on passive observations of traffic. We then apply this algorithm on packet traces collected at 55 end-hosts, and across different network environments. Our results show that 45 out of the 55 users we study experience bufferbloat at least once, 40% of these users experience bufferbloat more than once per hour. In 90% of cases, buffering more than doubles RTTs, but RTTs during bufferbloat are rarely over one second. We also show that web and interactive applications, which are particularly sensitive to delay, are the applications most often affected by bufferbloat

Cross-check of Analysis Modules and Reasoner Interactions

This deliverable presents an extended set of Analysis Modules, including both the improvements done to those presented in deliverable D4.1 as well as the new analysis algorithms designed and developed to address use-cases. The deliverable also describes a complete workflow description for the different use-cases, including both stream processing for real-time monitoring applications as well as batch processing for “off-line” analysis. This workflow description specifies the iterative interaction loop between WP2, WP3, T4.1, and T4.2, thereby allowing for a cross-checking of the analysis modules and the reasoner interactions.mPlane – an Intelligent Measurement Plane for Future Network and Application Managemen

Anycast census and geolocation

Anycast census and geolocation</p

Latency-Based Anycast Geolocalization: Algorithms, Software and Datasets .

<p>Use of IP-layer anycast has increased in the last few years beyond the DNS realm. Yet, existing measurement techniques to identify and enumerate anycast replicas exploit specifics of the DNS protocol, which limits their applicability to this particular service. With this paper, we not only propose and thoroughly validate a protocol-agnostic technique for anycast replicas discovery and geolocation, but also provide the community with open source software and datasets to replicate our experimental results, as well as facilitating the development of new techniques such as ours. In particular, our proposed method achieves thorough enumer-ation and city-level geolocalization of anycast instances from a set of known vantage points. The algorithm features an iterative workflow, pipelining enumeration (an optimization problem using latency as input) and geolocalization (a classification problem using side channel information such as city population) of anycast replicas. Results of a thorough validation campaign show our algorithm to be robust to measurement noise, and very lightweight as it requires only a handful of latency measurements. </p

A First Characterization of Anycast Traffic from Passive Traces .

IP anycast routes packets to the topologically nearestserver according to BGP proximity. In the last years, new playershave started adopting this technology to serve web content viaAnycast-enabled CDNs (A-CDN). To the best of our knowledge,in the literature, there are studies that focus on a specific A-CDNdeployment, but little is known about the users and the servicesthat A-CDNs are serving in the Internet at large.This prompted us to perform a passive characterization study,bringing out the principal A-CDN actors in our monitored setup,the services they offer, their penetration, etc. Results show avery heterogeneous picture, with A-CDN empowered servicesthat are very popular (e.g., Twitter or Bing), serve a lot ofdifferent contents (e.g., Wordpress or adult content), and eveninclude audio/video streaming (e.g., Soundcloud, or Vine). Ourmeasurements show that the A-CDN technology is quite matureand popular, with more than 50% of web users that accesscontent served by a A-CDN during peak tim