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Inferring multi-lateral peering
- in Proceedings of ACM CoNEXT
, 2013
"... The AS topology incompleteness problem is derived from difficulties in the discovery of p2p links, and is amplified by the increasing popularity of Internet eXchange Points (IXPs) to support peering interconnection. We describe, implement, and validate a method for discovering currently invisible IX ..."
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The AS topology incompleteness problem is derived from difficulties in the discovery of p2p links, and is amplified by the increasing popularity of Internet eXchange Points (IXPs) to support peering interconnection. We describe, implement, and validate a method for discovering currently invisible IXP peering links by mining BGP communities used by IXP route servers to implement multilateral peering (MLP), including communities that signal the intent to re-strict announcements to a subset of participants at a given IXP. Using route server data juxtaposed with a mapping of BGP community values, we can infer 206K p2p links from 13 large European IXPs, four times more p2p links than what is directly observable in public BGP data. The ad-vantages of the proposed technique are threefold. First, it utilizes existing BGP data sources and does not require the deployment of additional vantage points nor the acquisition of private data. Second, it requires only a few active queries, facilitating repeatability of the measurements. Finally, it of-fers a new source of data regarding the dense establishment
Are We One Hop Away from a Better Internet?
"... The Internet suffers from well-known performance, reliability, and security problems. However, proposed improvements have seen lit-tle adoption due to the difficulties of Internet-wide deployment. We observe that, instead of trying to solve these problems in the general case, it may be possible to m ..."
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The Internet suffers from well-known performance, reliability, and security problems. However, proposed improvements have seen lit-tle adoption due to the difficulties of Internet-wide deployment. We observe that, instead of trying to solve these problems in the general case, it may be possible to make substantial progress by focusing on solutions tailored to the paths between popular content providers and their clients, which carry a large share of Internet traffic. In this paper, we identify one property of these paths that may provide a foothold for deployable solutions: they are often very short. Our measurements show that Google connects directly to networks hosting more than 60 % of end-user prefixes, and that other large content providers have similar connectivity. These direct paths open the possibility of solutions that sidestep the headache of Internet-wide deployability, and we sketch approaches one might take to improve performance and security in this setting.
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"... Big Data is a hot topic, and the Internet is one of the few sources where it is possible to collect large amounts of data. It is not surprising then to see researchers trying to exploit Big Data techniques to analyze Internet data. This work goes in this direction, and applies Big Data methodologies ..."
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Big Data is a hot topic, and the Internet is one of the few sources where it is possible to collect large amounts of data. It is not surprising then to see researchers trying to exploit Big Data techniques to analyze Internet data. This work goes in this direction, and applies Big Data methodologies to net-work monitoring and management. Authors propose Datix, a fully decentralized network traffic analytics engine for querying very large datasets using existing map-reduce infrastructures. The key contribution is the ability to do efficient distributed joins between network traffic data (in this case, SFlow packet samples) and metadata about fields in that data (e.g. IP to AS number mappings), a key primitive operation in many network traffic analysis studies. The data model is a star schema with a large log table and smaller dimension tables, which are partitioned by keys on load time. At runtime, queries are mapped to relevant partitions that contain the data, and the resulting query is passed to Shark or Hive for execution. The result is a fast and scalable system that results particu-larly suited for the analysis of network management traces. Reviewers found this paper to be interesting, well motivated, even if incremental. Despite the lim-ited novelty of the proposed work, reviewers found Datix to be an important contribution, allow-ing existing infrastructure to be applied to very common network measurement tasks-- for which MapReduce is somewhat underutilized in practice. Plus, Datix is Open Source and available on GitHub. Public review written by
Datix: A System for Scalable Network Analytics
"... The ever-increasing Internet traffic poses challenges to net-work operators and administrators that have to analyze large network datasets in a timely manner to make decisions re-garding network routing, dimensioning, accountability and security. Network datasets collected at large networks such as ..."
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The ever-increasing Internet traffic poses challenges to net-work operators and administrators that have to analyze large network datasets in a timely manner to make decisions re-garding network routing, dimensioning, accountability and security. Network datasets collected at large networks such as Internet Service Providers (ISPs) or Internet Exchange Points (IXPs) can be in the order of Terabytes per hour. Un-fortunately, most of the current network analysis approaches are ad-hoc and centralized, and thus not scalable. In this paper, we present Datix, a fully decentralized, open-source analytics system for network traffic data that relies on smart partitioning storage schemes to support fast join algorithms and efficient execution of filtering queries. We outline the architecture and design of Datix and we present the evaluation of Datix using real traces from an operational IXP. Datix is a system that deals with an im-portant problem in the intersection of data management and network monitoring while utilizing state-of-the-art dis-tributed processing engines. In brief, Datix manages to ef-ficiently answer queries within minutes compared to more than 24 hours processing when executing existing Python-based code in single node setups. Datix also achieves nearly 70 % speedup compared to baseline query implementations of popular big data analytics engines such as Hive and Shark.
Peering at Peerings: On the Role of IXP Route Servers
"... During the last few years, more and more of the medium-to-large Internet eXchange Points (IXP) around the world have started to operate a route server and offer its use as a free value-added service to their members. This service has greatly simplified inter-domain routing for those members and has ..."
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During the last few years, more and more of the medium-to-large Internet eXchange Points (IXP) around the world have started to operate a route server and offer its use as a free value-added service to their members. This service has greatly simplified inter-domain routing for those members and has made it easy for them to peer with possibly hundreds of networks at those IXPs from the get-go. In this paper, we report on an empirical analysis that is based on a unique collection of IXP-provided datasets from two different European IXPs that operate a route server and gave us access to a wealth of route server-specific BGP data. Both IXPs also made the traffic datasets that they routinely collect from their public switch-ing infrastructures available to us. Using this information, we per-form a first-of-its-kind study that correlates a detailed control plane view with a rich data plane view to reason about the different peer-ing options available at these IXPs and how some of the major In-ternet players make use of them. In the process, we highlight the important role that the IXPs ’ route servers play for inter-domain routing in today’s Internet and demonstrate the benefits of studying IXP peerings in a manner that is not agnostic but fully aware of traffic. We conclude with a discussion of some of the ramifications of our findings for both network researchers and operators.
Summary: Mapping Interconnection in the Internet: Colocation, Connectivity and Congestion
"... As the global Internet expands to satisfy the demands and expectations of an ever-increasing frac-tion of the world's population, profound changes are occurring in its interconnection structure, trafc dynamics, and the economic and political power of different players in the ecosystem. These ch ..."
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As the global Internet expands to satisfy the demands and expectations of an ever-increasing frac-tion of the world's population, profound changes are occurring in its interconnection structure, trafc dynamics, and the economic and political power of different players in the ecosystem. These changes not only impact network engineering and operations, but also present broader challenges for technology investment, future network design, public policy, and scientic study of the Inter-net itself. And yet, from both scientic and policy perspectives, the evolving ecosystem is largely uncharted territory. In particular, two related transformations of the ecosystem motivate our inquiry: the emer-gence of Internet exchanges (IXes) as anchor points in the mesh of interconnection, and content providers and Content Delivery Networks (CDNs) as major sources of trafc owing into the In-ternet. By some accounts over half the trafc volume in North America now comes from just two content distributors (Youtube and Netix). This shift constitutes the rise of a new kind of hier-archy in the ecosystem, bringing fundamentally new constraints on existing players who need to manage trafc on their networks to minimize congestion. Measurement challenges limit our cur-rent capability to describe and understand these dynamics, but evidence of trouble has increased
Back-Office Web Traffic on The Internet
"... Although traffic between Web servers and Web browsers is read-ily apparent to many knowledgeable end users, fewer are aware of the extent of server-to-server Web traffic carried over the public Internet. We refer to the former class of traffic as front-office In-ternet Web traffic and the latter as ..."
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Although traffic between Web servers and Web browsers is read-ily apparent to many knowledgeable end users, fewer are aware of the extent of server-to-server Web traffic carried over the public Internet. We refer to the former class of traffic as front-office In-ternet Web traffic and the latter as back-office Internet Web traffic (or just front-office and back-office traffic, for short). Back-office traffic, which may or may not be triggered by end-user activity, is essential for today’s Web as it supports a number of popular but complex Web services including large-scale content delivery, so-cial networking, indexing, searching, advertising, and proxy ser-vices. This paper takes a first look at back-office traffic, measuring it from various vantage points, including from within ISPs, IXPs, and CDNs. We describe techniques for identifying back-office traf-fic based on the roles that this traffic plays in the Web ecosystem. Our measurements show that back-office traffic accounts for a sig-nificant fraction not only of core Internet traffic, but also of Web transactions in the terms of requests and responses. Finally, we dis-cuss the implications and opportunities that the presence of back-office traffic presents for the evolution of the Internet ecosystem.
