Implementation and Deployment of a Distributed Network Topology Discovery Algorithm

In the past few years, the network measurement community has been interested in the problem of internet topology discovery using a large number (hundreds or thousands) of measurement monitors. The standard way to obtain information about the internet topology is to use the traceroute tool from a small number of monitors. Recent papers have made the case that increasing the number of monitors will give a more accurate view of the topology. However, scaling up the number of monitors is not a trivial process. Duplication of effort close to the monitors wastes time by reexploring well-known parts of the network, and close to destinations might appear to be a distributed denial-of-service (DDoS) attack as the probes converge from a set of sources towards a given destination. In prior work, authors of this report proposed Doubletree, an algorithm for cooperative topology discovery, that reduces the load on the network, i.e., router IP interfaces and end-hosts, while discovering almost as many nodes and links as standard approaches based on traceroute. This report presents our open-source and freely downloadable implementation of Doubletree in a tool we call traceroute@home. We describe the deployment and validation of traceroute@home on the PlanetLab testbed and we report on the lessons learned from this experience. We discuss how traceroute@home can be developed further and discuss ideas for future improvements

Retouched Bloom Filters: Allowing Networked Applications to Flexibly Trade Off False Positives Against False Negatives

Where distributed agents must share voluminous set membership information, Bloom filters provide a compact, though lossy, way for them to do so. Numerous recent networking papers have examined the trade-offs between the bandwidth consumed by the transmission of Bloom filters, and the error rate, which takes the form of false positives, and which rises the more the filters are compressed. In this paper, we introduce the retouched Bloom filter (RBF), an extension that makes the Bloom filter more flexible by permitting the removal of selected false positives at the expense of generating random false negatives. We analytically show that RBFs created through a random process maintain an overall error rate, expressed as a combination of the false positive rate and the false negative rate, that is equal to the false positive rate of the corresponding Bloom filters. We further provide some simple heuristics and improved algorithms that decrease the false positive rate more than than the corresponding increase in the false negative rate, when creating RBFs. Finally, we demonstrate the advantages of an RBF over a Bloom filter in a distributed network topology measurement application, where information about large stop sets must be shared among route tracing monitors.Comment: This is a new version of the technical reports with improved algorithms and theorical analysis of algorithm

Path Similarity Evaluation using Bloom Filters

The performance of several Internet applications often relies on the measurability of path similarity between different participants. In particular, the performance of content distribution networks mainly relies on the awareness of content sources topology information. It is commonly admitted nowadays that, in order to ensure either path redundancy or efficient content replication, topological similarities between sources is evaluated by exchanging raw traceroute data, and by a hop by hop comparison of the IP topology observed from the sources to the several hundred or thousands of destinations. In this paper, based on real data we collected, we advocate that path similarity comparisons between different Internet entities can be much simplified using lossy coding techniques, such as Bloom filters, to exchange compressed topology information. The technique we introduce to evaluate path similarity enforces both scalability and data confidentiality while maintaining a high level of accuracy. In addition, we demonstrate that our technique is scalable as it requires a small amount of active probing and is not targets dependent

claffy, k.: Implementation and deployment of a distributed network topology discovery algorithm. cs.NI 0603062, arXiv

Abstract — In the past few years, the network measurement community has been interested in the problem of internet topology discovery using a large number (hundreds or thousands) of measurement monitors. The standard way to obtain information about the internet topology is to use the traceroute tool from a small number of monitors. Recent papers have made the case that increasing the number of monitors will give a more accurate view of the topology. However, scaling up the number of monitors is not a trivial process. Duplication of effort close to the monitors wastes time by reexploring well-known parts of the network, and close to destinations might appear to be a distributed denialof-service (DDoS) attack as the probes converge from a set of sources towards a given destination. In prior work, authors of this report proposed Doubletree, an algorithm for cooperative topology discovery, that reduces the load on the network, i.e., router IP interfaces and end-hosts, while discovering almost as many nodes and links as standard approaches based on traceroute. This report presents our open-source and freely downloadable implementation of Doubletree in a tool we cal

On the Quality of BGP Route Collectors for iBGP Policy Inference

peer reviewedA significant portion of what is known about Internet routing stems out from public BGP datasets. For this reason, numerous research efforts were devoted to (i) assessing the (in)completeness of the datasets, (ii) identifying biases in the dataset, and (iii) augmenting data quality by optimally placing new collectors. However, those studies focused on techniques to extract information about the AS-level Internet topology. In this paper, we show that considering different metrics influences the conclusions about biases and collector placement. Namely, we compare AS-level topology discovery with \iac inference. We find that the same datasets exhibit significantly diverse biases for these two metrics. For example, the sensitivity to the number and position of collectors is noticeably different. Moreover, for both metrics, the marginal utility of adding a new collector is strongly localized with respect to the proximity of the collector. Our results suggest that the ``optimal'' position for new collectors can only be defined with respect to a specific metric, hence posing a fundamental trade-off for maximizing the utility of extensions to the BGP data collection infrastructure

Algorithms for large-scale topology discovery

Ces dernières années ont vu un intérêt croissant dans la découverte, à grande échelle, de la topologie d'internet au niveau des interfaces IP. Une nouvelle génération de systèmes de mesure fortement distribués est actuellement en train d'être déployée. Malheureusement, avant cette thèse, la communauté de la recherche ne s'est pas attardée sur la question de savoir comment effectuer ces mesures de manière efficace. Dans cette thèse, nous proposons plusieurs contributions pour permettre aux mesures de passer à l'échelle. D'abord, nous montrons que les méthodes classiques de découverte de topologie (i.e., skitter) sont inefficaces car elles sondent de manière répétée les mêmes interfaces. De telles méthodes peuvent poser problème lors du passage à l'échelle car le trafic généré pourrait aisément ressembler à une attaque de déni de service distribué. Nous mesurons deux types de redondance dans l'envoi de sondes (intra- et inter-moniteur) et montrons qu'ils sont tous deux élevés. Dans un deuxième temps, comme il n'est pas inhabituel pour un moniteur de traceroute d'opérer en isolation, nous proposons et évaluons des stratégies pour réduire la redondance à l'intérieur d'un seul moniteur. L'idée clé de ces stratégies est de commencer à sonder loin du moniteur et de fonctionner en arrière. Nos résultats montrent que nos algorithmes peuvent diminuer la redondance au prix d'une légère réduction dans la découverte de noeuds et de liens. Ensuite, nous proposons et évaluons Doubletree, un algorithme coopératif qui réduit simultanément les deux types de redondance sur les routeurs et les systèmes finaux. Les idées clés sont (i) exploiter les structures en arbres des routes vers et depuis un seul point afin de décider quand arrêter d'envoyer des sondes, (ii) commencer à sonder quelque part au milieu du chemin. Nos résultats montrent que Doubletree peut réduire les deux types de charge sur le réseau de manière drastique tout en permettant de découvrir presque le même ensemble de noeuds et de liens. En plus, nous fournissons et évaluons une implémentation de Doubletree dans un outil appelé traceroute@home. Quatrièmement, nous fournissons plusieurs améliorations à Doubletree afin de réduire le coût de communication, la charge sur les destinations et augmenter la couverture. Enfin, nous proposons une généralisation des filtres de Bloom, les retouched Bloom filters, qui permet des faux négatifs en plus des faux positifs ainsi qu'un moyen d'obtenir un compromis entre les deux. Nous montrons aussi comment diminuer le taux d'erreur global, en l'exprimant comme une combinaison entre les taux de faux positifs et de faux négatifs.PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF