Despite the architectural separation between intradomain and interdomain routing in the Internet, intradomain protocols do influence the path-selection process in the Border Gateway Protocol (BGP). When choosing between multiple equally-good BGP routes, a router selects the one with the closest egress point, based on the intradomain path cost. Under such hot-potato routing, an intradomain event can trigger BGP routing changes. To characterize the influence of hot-potato routing, we conduct controlled experiments with a commercial router. Then, we propose a technique for associating BGP routing changes with events visible in the intradomain protocol, and apply our algorithm to AT&T's backbone network. We show that (i) hot-potato routing can be a significant source of BGP updates, (ii) BGP updates can lag ¢ ¡ seconds or more behind the intradomain event, (iii) the number of BGP path changes triggered by hot-potato routing has a nearly uniform distribution across destination prefixes, and (iv) the fraction of BGP messages triggered by intradomain changes varies significantly across time and router locations. We show that hot-potato routing changes lead to longer delays in forwarding-plane convergence, shifts in the flow of traffic to neighboring domains, extra externally-visible BGP update messages, and inaccuracies in Internet performance measurements.

Hot-potato routing is a mechanism employed when there are multiple (equally good) interdomain routes available for a given destination. In this scenario, the Border Gateway Protocol (BGP) selects the interdomain route associated with the closest egress point based upon intradomain path costs. Consequently, intradomain routing changes can impact interdomain routing and cause abrupt swings of external routes, which we call hot-potato disruptions. Recent work has shown that hot-potato disruptions can have a substantial impact on large ISP backbones and thereby jeopardize the network robustness. As a result, there is a need for guidelines and tools to assist in the design of networks that minimize hot-potato disruptions. However, developing these tools is challenging due to the complex and subtle nature of the interactions between exterior and interior routing. In this paper, we address these challenges using an analytic model of hot-potato routing that incorporates metrics to evaluate network sensitivity to hot-potato disruptions. We then present a methodology for computing these metrics using measurements of real ISP networks. We demonstrate the utility of our model by analyzing the sensitivity of a large AS in a tier 1 ISP network.

Hot-potato routing is a mechanism employed when there are multiple (equally good) interdomain routes available for a given destination. In this scenario, the Border Gateway Protocol (BGP) selects the interdomain route associated with the closest egress point based upon intradomain path costs. Consequently, intradomain routing changes can impact interdomain routing and cause abrupt swings of external routes, which we call hot-potato disruptions. Recent work has shown that hot-potato disruptions can have a substantial impact on large ISP backbones and thereby jeopardize the network robustness. As a result, there is a need for guidelines and tools to assist in the design of networks that minimize hot-potato disruptions. However, developing these tools is challenging due to the complex and subtle nature of the interactions between exterior and interior routing. In this paper, we address these challenges using an analytic model of hot-potato routing that incorporates metrics to evaluate network sensitivity to hot-potato disruptions. We then present a methodology for computing these metrics using measurements of real ISP networks. We demonstrate the utility of our model by analyzing the sensitivity of a large AS in a tier 1 ISP network.

IP forwarding anomalies, triggered by equipment failures, implementation bugs, or configuration errors, can significantly disrupt and degrade network service. Robust and reliable detection of such anomalies is essential to rapid problem diagnosis, problem mitigation, and repair. We propose a simple, robust method that integrates routing and traffic data streams to reliably detect forwarding anomalies, and report on the evaluation of the method in a tier-1 ISP backbone. First, we transform each data stream separately, to produce informative alarm indicators. A forwarding anomaly is then signalled only if the indicators for both streams indicate anomalous behavior concurrently. The overall method is scalable, automated and self-training. We find this technique effectively identifies forwarding anomalies, while avoiding the high false alarms rate that would otherwise result if either stream were used unilaterally.

IP forwarding anomalies, triggered by equipment failures, implementation bugs, or configuration errors, can significantly disrupt and degrade network service. Robust and reliable detection of such anomalies is essential to rapid problem diagnosis, problem mitigation, and repair. We propose a simple, robust method that integrates routing and traffic data streams to reliably detect forwarding anomalies, and report on the evaluation of the method in a tier-1 ISP backbone. First, we transform each data stream separately, to produce informative alarm indicators. A forwarding anomaly is then signaled only if the indicators for both streams indicate anomalous behavior concurrently. The overall method is scalable, automated and self-training. We find this technique effectively identifies forwarding anomalies, while avoiding the high false alarms rate that would otherwise result if either stream were used unilaterally.