You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an unsupervised technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Constellation Distance, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allow comparison between the clustering results from the same dataset, no technique allows to measure the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Constellation Distance, to solve this problem. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies which actually impair the QoE that the end-users perceive.

Cited Abstract(s):
[1]: 

[2]: Modern content-distribution networks both provide bulk content and act as "serving infrastructure" for web services in order to reduce user-perceived latency. Serving infrastructures such as Google's are now critical to the online economy, making it imperative to understand their size, geographic distribution, and growth strategies. To this end, we develop techniques that enumerate IP addresses of servers in these infrastructures, find their geographic location, and identify the association between clients and clusters of servers. While general techniques for server enumeration and geolocation can exhibit large error, our techniques exploit the design and mechanisms of serving infrastructure to improve accuracy. We use the EDNS-client-subnet DNS extension to measure which clients a service maps to which of its serving sites. We devise a novel technique that uses this mapping to geolocate servers by combining noisy information about client locations with speed-of-light constraints. We demonstrate that this technique substantially improves geolocation accuracy relative to existing approaches. We also cluster server IP addresses into physical sites by measuring RTTs and adapting the cluster thresholds dynamically. Google's serving infrastructure has grown dramatically in the ten months, and we use our methods to chart its growth and understand its content serving strategy. We find that the number of Google serving sites has increased more than sevenfold, and most of the growth has occurred by placing servers in large and small ISPs across the world, not by expanding Google's backbone.

[3]: We deduce key design features behind the YouTube video delivery system by building a distributed active measurement infrastructure, and collecting and analyzing a large volume of video playback logs, DNS mappings and latency data. We find that the design of YouTube video delivery system consists of three major components: a “flat” video id space, multiple DNS namespaces reflecting a multi-layered logical organization of video servers, and a 3-tier physical cache hierarchy. We also uncover that YouTube employs a set of sophisticated mechanisms to handle video delivery dynamics such as cache misses and load sharing among its distributed cache locations and data centers.

[4]: 

[5]: 

[6]: 

[7]: Identifying applications and classifying network traffic flows according to their source applications are critical for a broad range of network activities. Such a decision can be based on packet header fields, packet payload content, statistical characteristics of traffic and communication patterns of network hosts. However, most present techniques rely on some sort of a priori knowledge, which means they require labor-intensive preprocessing before running and cannot deal with previously unknown applications. In this paper, we propose a traffic classification system based on application signatures, with a novel approach to fully automate the process of deriving signatures from unidentified traffic. The key idea is to integrate statistics-based flow clustering with payload-based signature matching method, so as to eliminate the requirement of pre-labeled training data sets. We evaluate the efficiency of our approach using real-world traffic trace, and the results indicate that signature classifiers built from clustered data and pre-labeled data are able to achieve similar high accuracy better than 99p. Copyright © 2010 John Wiley & Sons, Ltd.

[8]: 

[9]: 

[10]: In this paper, we conduct a detailed study of the YouTube CDN with a view to understanding the mechanisms and policies used to determine which data centers users download video from. Our analysis is conducted using week-long datasets simultaneously collected from the edge of five networks - two university campuses and three ISP networks - located in three different countries. We employ state-of-the-art delay-based geolocation techniques to find the geographical location of YouTube servers. A unique aspect of our work is that we perform our analysis on groups of related YouTube flows. This enables us to infer key aspects of the system design that would be difficult to glean by considering individual flows in isolation. Our results reveal that while the RTT between users and data centers plays a role in the video server selection process, a variety of other factors may influence this selection including load-balancing, diurnal effects, variations across DNS servers within a network, limited availability of rarely accessed video, and the need to alleviate hot-spots that may arise due to popular video content.

[11]: 

[12]: This chapter investigates HTTP video streaming over the Internet for the YouTube platform. YouTube is used as concrete example and case study for video delivery over the Internet, since it is not only the most popular online video platform, but also generates a large share of traffic on today's Internet. We will describe the YouTube infrastructure as well as the underlying mechanisms for optimizing content delivery. Such mechanisms include server selection via DNS as well as application-layer traffic management. Furthermore, the impact of delivery via the Internet on the user experienced quality (QoE) of YouTube video streaming is quantified. In this context, different QoE monitoring approaches are qualitatively compared and evaluated in terms of the accuracy of QoE estimation.
Considering the YouTube delivery infrastructure, a large body of work verified its evolution over time [6] [6]9 [9] [9]5 [5] [1] . They show a highly dynamic system which keeps changing over time due to continuous upgrades in the infrastructure [2] [3] or due to the dynamicity of the cache selection policies [6]3 [4] [7] [6]1 . Some of the findings are already outdated. For instance, the load-balancing policy based on HTTP redirections which is described in [6]3 [6]1 is no longer in place, and YouTube dismissed the naming scheme described in [3] at the end of 2011. In this work, we do not aim to offer an updated view of YouTube. We rather aim at offering a methodology that allows to automatically identify changes in both the infrastructure, e.g., the appearance of new , and in the day to day management of the infrastructure, e.g., a change in the load-balancing algorithm that may affect millions of customers.