Cloud and AI/ML workloads are posing unprecedented new requirements on the wide-area network: it must combine strict availability, massive growth, and feature agility. It became increasingly clear that traditional WAN designs were ill-equipped to adapt to these requirements.

Motivated by our experiences operating a global WAN, we argue that SDN's reliance on infrastructure external to the data plane has substantially complicated the challenge of maintaining high availability. We propose a new decentralized SDN (dSDN) architecture in which SDN control logic instead runs within routers, eliminating the control plane's reliance on external infrastructure and restoring fate-sharing between control and data planes. We present dSDN as a simpler approach to realizing the benefits of SDN in the WAN. Despite its much simpler design, we show that dSDN is practical from an implementation viewpoint, and outperforms centralized SDN in terms of routing convergence and SLO impact.

Private WANs are increasingly important to the operation of enterprises, telecoms, and cloud providers. For example, B4, Google's private software-defined WAN, is larger and growing faster than our connectivity to the public Internet. In this paper, we present the five-year evolution of B4. We describe the techniques we employed to incrementally move from offering best-effort content-copy services to carrier-grade availability, while concurrently scaling B4 to accommodate 100x more traffic. Our key challenge is balancing the tension introduced by hierarchy required for scalability, the partitioning required for availability, and the capacity asymmetry inherent to the construction and operation of any large-scale network. We discuss our approach to managing this tension: i) we design a custom hierarchical network topology for both horizontal and vertical software scaling, ii) we manage inherent capacity asymmetry in hierarchical topologies using a novel traffic engineering algorithm without packet encapsulation, and iii) we re-architect switch forwarding rules via two-stage matching/hashing to deal with asymmetric network failures at scale.

We present the design, implementation, and evaluation of B4, a private WAN connecting Google's data centers across the planet. B4 has a number of unique characteristics: i) massive bandwidth requirements deployed to a modest number of sites, ii) elastic traffic demand that seeks to maximize average bandwidth, and iii) full control over the edge servers and network, which enables rate limiting and demand measurement at the edge.

One of the goals of traffic engineering is to achieve a flexible trade-off between fairness and throughput so that users are satisfied with their bandwidth allocation and the network operator is satisfied with the utilization of network resources. In this paper, we propose a novel way to balance the throughput and fairness objectives with linear programming. It allows the network operator to precisely control the trade-off by bounding the fairness degradation for each commodity compared to the max-min fair solution or the throughput degradation compared to the optimal throughput. We also present improvements to a previous algorithm that achieves max-min fairness by solving a series of linear programs. We significantly reduce the number of steps needed when the access rate of commodities is limited. We extend the algorithm to two important practical use cases: importance weights and piece-wise linear utility functions for commodities. Our experiments on synthetic and real networks show that our algorithms achieve a significant speedup and provide practical insights on the trade-off between fairness and throughput.