We describe a new system for distributed tracing at the IP level of the routes that packets take through the IPv4 internet. Our Zeph algorithm coordinates route tracing efforts across agents at multiple vantage points, assigning to each agent a number of /24 destination prefixes in proportion to its probing budget and chosen according to a reinforcement learning heuristic that aims to maximize the number of multipath links discovered. Zeph runs on top of Iris, our fault tolerant system for orchestrating internet measurements across distributed agents of heterogeneous probing capacities. Iris is built around third party free open source software and modern containerization technology, thereby presenting a new model for assembling a resilient and maintainable internet measurement architecture. We show that carefully choosing the destinations to probe from which vantage point matters to optimize topology discovery and that a system can learn which assignment will maximize the overall discovery based on previous measurements. After 10 cycles of probing, Zeph is capable of discovering 2.4M nodes and 10M links in a cycle of 6 hours, when deployed on 5 Iris agents. This is at least 2 times more nodes and 5 times more links than other production systems for the same number of prefixes probed.

我们描述了一个新系统，用于在数据包通过 IPv4 互联网的路由的 IP 级别进行分布式跟踪。我们的 Zeph 算法在多个有利位置协调跨代理的路由跟踪工作，为每个代理分配与其探测预算成比例的 /24 个目的地前缀，并根据旨在最大化发现的多路径链接数量的强化学习启发式方法进行选择。Zeph 运行在 Iris 之上，这是我们的容错系统，用于跨具有异构探测能力的分布式代理协调互联网测量。Iris 是围绕第三方免费开源软件和现代容器化技术构建的，从而为组装弹性和可维护的互联网测量架构提供了一种新模型。我们表明，仔细选择目的地以从哪个有利位置进行探测对于优化拓扑发现很重要，并且系统可以根据先前的测量来了解哪个分配将最大化整体发现。经过 10 个探测周期后，Zeph 部署在 5 个 Iris 代理上时，能够在 6 小时的周期内发现 240 万个节点和 1000 万个链接。对于探测的相同数量的前缀，这比其他生产系统至少多 2 倍和多 5 倍的链接。