Most deployed WAN Traffic Engineering (TE) systems use a logically centralized controller that periodically gathers traffic demands, runs a TE optimization or heuristic, and then programs the network. At scale, these solutions are often suboptimal and can take minutes to react to demand changes or failures. In this paper, we introduce OnlineTE, a system that reacts immediately to demand changes and failures and delivers near-optimal solutions within seconds of a change. OnlineTE builds on the theory of optimization decomposition to devise scalable, near-optimal, distributed TE solvers for path-based MLU and Max-Flow problems. In OnlineTE, switches each solve a local subproblem, and a central coordinator coordinates their convergence. As such, a switch can trigger a re-optimization as soon as it detects a demand change or failure, enabling high reactivity. OnlineTE scales to large WANs, and its computational requirements are well within the capabilities of modern WAN switches. It also enables a novel paradigm, edge-based TE, which can utilize resources more efficiently than today’s path-based approaches. On a testbed emulation of a 750-node WAN topology, OnlineTE outperforms the state-of-the-art by up to an order of magnitude.

Today, large-scale software-defined networks use microservice-based controllers. Bugs in these controllers can reduce network availability by making the data plane state inconsistent with the high-level intent. To recover from such inconsistencies, modern controllers periodically reconcile the state of all the switches with the desired intent. However, periodic reconciliation limits the availability and performance of the network at scale. We introduce Zenith, a microservice-based controller that avoids inconsistencies by design rather than always relying on recovery mechanisms. We have formally verified Zenith’s specifications and have proved that it ensures the network state will eventually be consistent with intent. We automatically generate Zenith’s code from its specification to minimize the likelihood of errors in the final implementation. Zenith’s guarantees and abstractions also enable developers to independently verify SDN applications and ensure end-to-end safety and correctness. Zenith resolves inconsistencies 5\texttimes faster than today’s designs and significantly improves availability.

In this paper, we consider a new workload for which serverless platforms are well-suited: the execution of a 3D printer controller in the cloud. This workload is qualitatively different from those considered in prior work due to the stringent timing requirements. Our measurements on popular serverless platforms reveal millisecond-level overheads that impair the timely execution of the example control algorithm we consider. To mitigate the impact of these overheads, we judiciously partition the execution of the algorithm across a set of serverless functions and exploit timely speculation. Our evaluations on AWS Lambda show that, for 30 diverse print jobs, Cosmic is able to ensure the timely execution of the controller while reducing cost by 2.8x–3.5x compared to other approaches.