Characterizing User-to-User Connectivity with RIPE Atlas

Characterizing the interconnectivity of networks at a country level is an interesting but non-trivial task. The IXP Country Jedi is an existing prototype that uses RIPE Atlas probes in order to explore interconnectivity at a country level, taking into account all Autonomous Systems (AS) where RIPE Atlas probes are deployed. In this work, we build upon this basis and specifically focus on "eyeball" networks, i.e. the user-facing networks with the largest user populations in any given country, and explore to what extent we can provide insights on their interconnectivity. In particular, with a focused user-to-user (and/or user-to-content) version of the IXP Country Jedi we work towards meaningful statistics and comparisons between countries/economies. This is something that a general-purpose probe-to-probe version is not able to capture. We present our preliminary work on the estimation of RIPE Atlas coverage in eyeball networks, as well as an approach to measure and visualize user interconnectivity with our Eyeball Jedi tool.Comment: In Proceedings of the Applied Networking Research Workshop (ANRW '17

Caractérisation de la table de routage BGP

International audienceBGP routing table growth is one of the major Internet scaling issues, and prefix deaggregation is thought to be a major contributor to table growth. In this work we quantify the fragmentation of the routing table by the type of IP prefix. We observe that the proportion of deaggregated prefixes has quasi doubled in the last fifteen years. Our study also shows that the deaggregated prefixes are the least stable; they appear and disappear more frequently. While we can not see significant differences in path prepending between the categories, deaggregated prefixes do tend to be announced more selectively, indicating traffic engineering. We find cases where lonely prefixes are actually deaggregation in disguise. Indeed, some large transit ISPs advertise many lonely prefixes when they own the covering prefix. We show the extents of this practice that has a negative impact on the routing table even though it could usually be avoided.La croissance de la table de routage BGP est un des problèmes majeurs de l'expansion d'Internet, et la désaggrégation des préfixes semble être la cause principale de cette croissance. Dans cet article, nous quantifions la fragmentation de la table de routage BGP en classant les préfixes IP par type. Nous observons que la proportion de préfixes désaggrégés a doublé dans les quinze dernières années. Nous montrons également que ces préfixes sont les moins stables: ils apparaissent et disparaissent plus fréquemment. Malgrés le taux similaire de path prepending pour les différentes catégories de préfixes, les préfixes désaggrégés ont tendance à être annoncés sélectivement, indiquant de l'ingénierie de trafic. Une partie des préfixes solitaires sont en réalité désaggrégés. En effet, certains grands FAI annoncent un grand nombre de préfixes solitaires alors qu'ils possèdent le préfixe les couvrant. Nous dévoilons l'étendue de cette pratique qui a un effet non négligeable sur la fragmentation de la table de routage alors qu'elle pourrait généralement être évitée

Bias in Internet Measurement Platforms

Network operators and researchers frequently use Internet measurement platforms (IMPs), such as RIPE Atlas, RIPE RIS, or RouteViews for, e.g., monitoring network performance, detecting routing events, topology discovery, or route optimization. To interpret the results of their measurements and avoid pitfalls or wrong generalizations, users must understand a platform's limitations. To this end, this paper studies an important limitation of IMPs, the \textit{bias}, which exists due to the non-uniform deployment of the vantage points. Specifically, we introduce a generic framework to systematically and comprehensively quantify the multi-dimensional (e.g., across location, topology, network types, etc.) biases of IMPs. Using the framework and open datasets, we perform a detailed analysis of biases in IMPs that confirms well-known (to the domain experts) biases and sheds light on less-known or unexplored biases. To facilitate IMP users to obtain awareness of and explore bias in their measurements, as well as further research and analyses (e.g., methods for mitigating bias), we publicly share our code and data, and provide online tools (API, Web app, etc.) that calculate and visualize the bias in measurement setups

Gaining insight into AS-level outages through analysis of internet background radiation

Abstract—Internet Background Radiation (IBR) is unsolicited network traffic mostly generated by malicious software, e.g., worms, scans. In previous work, we extracted a signal from IBR traffic arriving at a large (/8) segment of unassigned IPv4 address space to identify large-scale disruptions of connectivity at an Autonomous System (AS) granularity, and used our technique to study episodes of government censorship and natural disasters [1]. Here we explore other IBR-derived metrics that may provide insights into the causes of macroscopic connectivity disruptions. We propose metrics indicating packet loss (e.g., due to link congestion) along a path from a specific AS to our observation point. We use three case studies to illustrate how our metrics can help identify packet loss characteristics of an outage. These metrics could be used in the diagnostic component of a semi-automated system for detecting and characterizing large-scale outages. I

What Happens When You Let Statisticians Loose on RIPE Atlas Data?

RIPE Lab

What Happens When You Let Statisticians Loose on RIPE Atlas Data?

RIPE Lab

What Happens When You Let Statisticians Loose on RIPE Atlas Data?

RIPE Lab

An API for Summarising Events in RIPE Atlas RTT Time Series

RIPE Lab