BGP data collection platforms as currently architected face fundamental challenges that threaten their long-term sustainability. Inspired by recent work, we analyze, prototype, and evaluate a new optimization paradigm for BGP collection. Our system scales data collection with two components: analyzing redundancy between BGP updates and using it to optimize sampling of the incoming streams of BGP data. An appropriate definition of redundancy across updates depends on the analysis objective. Our contributions include: a survey, measurements, and simulations to demonstrate the limitations of current systems; a general framework and algorithms to assess and remove redundancy in BGP observations; and quantitative analysis of the benefit of our approach in terms of accuracy and coverage for several canonical BGP routing analyses such as hijack detection and topology mapping. Finally, we implement and deploy a new BGP peering collection system that automates peering expansion using our redundancy analytics, which provides a path forward for more thorough evaluation of this approach.

Dramatic growth in Internet connectivity poses a challenge for the resource-constrained data collection efforts that support scientific and operational analysis of interdomain routing. Inspired by tradeoffs made in other disciplines, we explore a fundamental reconceptualization to how we design public BGP data collection architectures: an overshoot-and-discard approach that can accommodate an order of magnitude increase in vantage points by discarding redundant data shortly after its collection. As defining redundant depends on the context, we design algorithms that filter redundant updates without optimizing for one objective, and evaluate our approach in terms of detecting two noteworthy phenomena using BGP data: AS-topology mapping and hijacks. Our approach can generalize to other types of Internet data (e.g., traceroute, traffic). We offer this study as a first step to a potentially new area of Internet measurement research.

Scrutinizing BGP routes is part of the everyday tasks that network operators and researchers conduct to monitor their networks and measure Internet routing. This task is facilitated by the expansion of routing information services such as RIPE RIS [2] and Route-Views [3] that collect BGP routes from an increasing number of Vantage Points (VPs). Unfortunately, while more data is often beneficial, in the case of BGP, it involves downloading and processing large volumes of route updates that exhibit a high level of redundancy. Today with more than one billion route updates collected every day, users often have no other option than to focus on a subset of the VP. Because of the highly skewed location of the VP, randomly selecting them may result in a lot of missing information.

Traditional network control planes can be slow and require manual tinkering from operators to change their behavior. There is thus great interest in a faster, data-driven approach that uses signals from real-time traffic instead. However, the promise of fast and automatic reaction to data comes with new risks: malicious inputs designed towards negative outcomes for the network, service providers, users, and operators.

Network operators often face the problem of remote outages in transit networks leading to significant (sometimes on the order of minutes) downtimes. The issue is that BGP, the Internet routing protocol, often converges slowly upon such outages, as large bursts of messages have to be processed and propagated router by router.

Public measurement platforms composed of low-end hardware devices such as RIPE Atlas have gained significant traction in the research community. Such platforms are indeed particularly interesting as they provide Internet-wide measurement capabilities together with an ever growing set of measurement tools. To be scalable though, they allow for concurrent measurements between users. This paper answers a fundamental question for any platform user: Do measurements launched by others impact my results? If so, what can I do about it?

By enabling logically-centralized and direct control of the forwarding behavior of a network, Software-Defined Networking (SDN) holds great promise in terms of improving network management, performance, and costs. Realizing this vision is challenging though as SDN proposals to date require substantial and expensive changes to the existing network architecture before the benefits can be realized. As a result, the number of SDN deployments has been rather limited in scope. To kickstart a wide-scale SDN deployment, there is a need for low-risk, high return solutions that solve a timely problem. As one possible solution, we show how we can significantly improve the performance of legacy IP routers, i.e. "supercharge" them, by combining them with SDN-enabled devices. In this abstract, we supercharge one particular aspect of the router performance: its convergence time after a link or a node failure.