Measuring ISP Topologies With Rocketfuel

Spring, Neil; Mahajan, Ratul; Wetherall, David; Anderson, Thomas E.

doi:10.1109/tnet.2003.822655

Cited by 1,038 publications

(620 citation statements)

References 23 publications

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“…The entries corresponding to the highest accuracies among these 10 indices are emphasized in black. The six real networks for testing are a protein-protein interaction network (PPI) [16], a co-authorship network of scientists who are themselves publishing on the topic of network science (NS) [50], an electrical power-grid of the western US (Grid) [51], a network of the US political blogs (PB) [52], a router-level Internet collected by Rocketfuel Project (INT) [53], and a network of the US air transportation system (USAir) [54]. Detailed information about these networks can be found in Ref.…”

Section: Local Similarity Indicesmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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Section: Local Similarity Indicesmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

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“…WhyHigh uses the geographic location of routers observed in the output of traceroute to determine if the path is circuitous. To do so, we use router DNS name to location mappings developed as part of the Rocketfuel project [20]. WhyHigh also attempts to group together prefixes affected by the same underlying cause, an approach used previously to diagnose BGP routing instabilities [5,22].…”

Section: Related Workmentioning

confidence: 99%

Moving beyond end-to-end path information to optimize CDN performance

Krishnan

Madhyastha

Srinivasan

et al. 2009

Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement

182

119

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Replicating content across a geographically distributed set of servers and redirecting clients to the closest server in terms of latency has emerged as a common paradigm for improving client performance.In this paper, we analyze latencies measured from servers in Google's content distribution network (CDN) to clients all across the Internet to study the effectiveness of latency-based server selection. Our main result is that redirecting every client to the server with least latency does not suffice to optimize client latencies. First, even though most clients are served by a geographically nearby CDN node, a sizeable fraction of clients experience latencies several tens of milliseconds higher than other clients in the same region. Second, we find that queueing delays often override the benefits of a client interacting with a nearby server.To help the administrators of Google's CDN cope with these problems, we have built a system called WhyHigh. First, WhyHigh measures client latencies across all nodes in the CDN and correlates measurements to identify the prefixes affected by inflated latencies. Second, since clients in several thousand prefixes have poor latencies, WhyHigh prioritizes problems based on the impact that solving them would have, e.g., by identifying either an AS path common to several inflated prefixes or a CDN node where path inflation is widespread. Finally, WhyHigh diagnoses the causes for inflated latencies using active measurements such as traceroutes and pings, in combination with datasets such as BGP paths and flow records. Typical causes discovered include lack of peering, routing misconfigurations, and side-effects of traffic engineering. We have used WhyHigh to diagnose several instances of inflated latencies, and our efforts over the course of a year have significantly helped improve the performance offered to clients by Google's CDN.

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“…However, it may be possible to make useful inferences without direct access to that data by leveraging existing techniques that measure networks' data and control planes from the outside. Many techniques already exist to infer network characteristics such as topology (e.g., Rocketfuel [18]) and device types (e.g., nmap [14]). Operational health of the network too can be observed externally, e.g., by regularly probing for failures (e.g., Hubble [9]) and observing the control messages that a network sends (e.g., BGP announcements [12]).…”

Section: New Inference Techniquesmentioning

confidence: 99%

“…To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. The three network planes characteristics [7], available bandwidth along a path [8], loss rate and re-ordering [11], network topology [18], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Call to Arms for Management Plane Analytics

Mahajan

2014

Proceedings of the 13th ACM Workshop on Hot Topics in Networks

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Over the last few decades, the networking community has developed numerous techniques for understanding how real networks behave through analyzing their data and control planes. In this paper, we call upon the community to similarly develop techniques to analyze the network management plane, that is, activities that underlie network design and operation. Such analytics can shed light on why a network behaves as observed and the relative merits of different management practices. While the management plane is often not directly observable, we argue that many relevant aspects can be inferred through data that most networks already gather (e.g., snapshots of configurations). Using preliminary analysis of such data from many large networks, we demonstrate the feasibility and the value of management plane analytics.

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Measuring ISP Topologies With Rocketfuel

Cited by 1,038 publications

References 23 publications

Link prediction in complex networks: A survey

Link prediction in complex networks: A survey

Moving beyond end-to-end path information to optimize CDN performance

A Call to Arms for Management Plane Analytics

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