TIPSY: Predicting Where Traffic Will Ingress a WAN

In addition to consumer workloads, public cloud providers host enterprise workloads such as video conferencing and AI+ML pipelines. Enterprise workloads can, at times, overwhelm the available ingress capacity on individual peering links. Traditional techniques to address this problem in the consumer setting do not always apply here, such as aggressive use of captchas, or use of CDN caches in eyeball networks.

Ingress congestion events necessitate shifting traffic to other peering links at short timescales. While content providers use such techniques in the egress direction, ingress is inherently a different and more challenging problem. Once a packet leaves an enterprise network, it is subject to opaque routing policies that influence the path to the cloud provider. With almost 100K public & private ASes, 500K interconnections between them, and 1M routable prefixes, it is infeasible to gather comprehensive and often private data to precisely determine the path a flow will take.

To address the problem of ingress congestion, we present TIPSY, a statistical-classification-based system for predicting the peering link through which a flow will enter a WAN. TIPSY’s predictions are used to safely operate a congestion mitigation system that injects BGP withdrawal messages to redirect traffic away from congested peering links. We train TIPSY on traffic data from one of the largest public cloud WANs, and we demonstrate 76% accuracy in predicting through which 3 peering links (out of thousands) a flow will enter the network after BGP withdrawals.