Configuration Migration Planning through Decoupled Modeling and Searching

Abstract

Executing large network configuration changes, called migrations, is slow and error-prone today; operators must manually devise a safe order for updating devices that does not disrupt traffic and meets operational intents (e.g., low total time). We develop Smoothie, a versatile migration planning framework that, unlike existing tools, can support diverse operational intents. Inspired by automated planning, it achieves versatility by decoupling the encoding of network safety and intents from the search for a viable plan. Smoothie uses symbolic simulation for general and scalable safety encoding, after which it can use a range of planners, including existing off-the-shelf planners and a new one we developed for optimized parallel plans. We demonstrate Smoothie's versatility and benchmark its performance using diverse migration scenarios on many topologies. It finds both sequential plans and parallel plans, and the parallel ones compress up to thousands of configuration updates into no more than 3 steps. Its versatility does not come at the expense of performance: finishing most planning tasks in less than 1 hour.