Scalable BGP Prefix Selection for Effective Inter-domain Traffic Engineering

Inter-domain Traffic Engineering for multi-homed networks faces a scalability challenge, as the size of BGP routing table continue to grow. In this context, the choice of the best path must be made potentially for each destination prefix, requiring all available paths to be characterised (e.g., through measurements) and compared with each other. Fortunately, it is well-known that a few number of prefixes carry the larger part of the traffic. As a natural consequence, to engineer large volume of traffic only few prefixes need to be managed. Yet, traffic characteristics of a given prefix can greatly vary over time, and little is known on the dynamism of traffic at this aggregation level, including predicting the set of the most significant prefixes in the near future. %based on past observations. Sophisticated prediction methods won't scale in such context. In this paper, we study the relationship between prefix volume, stability, and predictability, based on recent traffic traces from nine different networks. Three simple and resource-efficient methods to select the prefixes associated with the most important foreseeable traffic volume are then proposed. Such proposed methods allow to select sets of prefixes with both excellent representativeness (volume coverage) and stability in time, for which the best routes are identified. The analysis carried out confirm the potential benefits of a route decision engine

Improve round-trip time measurement quality via clustering in inter-domain traffic engineering

International audience; For multi-homed networks, inter-domain traffic engineering (TE) consists in selecting the best path via available transit providers, so that the transmission quality is improved in front of network events, such as congestion and fail-over. In practice, this choice bases on end-to-end (e2e) measurements toward destination networks. These measurements, especially Round-Trip Time (RTT), are expected to offer an faithful view on inter-domain path properties. Hosts in destination networks with open ports are deliberately discovered for active measurement. RTT traces so obtained can be influenced by host-local factors that are not relevant to inter-domain routing and eventually mislead route decisions. We data-mined the RTT time-series between two ASes with unsupervised learning method - clustering, on a set of statistic features. Achieved results showed that our method was capable of improving data quality, by excluding less reliable traces. Moreover, we considered traceroute measurements. Early results suggested that most variations of e2e delay actually occured in access networks. We thus believe that the proposed scheme can improve the accuracy and stability of the route selection for multi-homed networks

Scalable BGP Prefix Selection for Effective Inter-domain Traffic Engineering

International audience<p>Inter-domain Traffic Engineering for multi-homed networks faces a scalability challenge, as the size of BGP routing table continue to grow. In this context, the choice of the best path must be made potentially for each destination prefix, requiring all available paths to be characterized (e.g., through measurements) and compared with each other.Fortunately, it is well-known that a few number of prefixes could carry a dominant part of the traffic. As a natural consequence, to engineer a majority of traffic only a handful of prefixes need to be managed. Yet, traffic characteristics of a given prefix can vary greatly over time, which requires us to predict most significant prefixes in the near future. Moreover, little is known on the traffic dynamism at this aggregation level and sophisticated prediction methods won’t scale in such context.In this paper, we study the relationship between prefix volume, stability, and predictability, based on recent traffic traces from nine different networks.Three simple and resource-efficient methods to se- lect the prefixes associated with the most important foreseeable traffic volume are then proposed. Such proposed methods are capable of select sets of prefixes with both excellent representativeness (volume cover- age) and stability in time, for which the best routes are identified. The analysis carried out confirms the potential benefits of a route decision engine.</p