We present Vercel, a network verification and automatic fault rectification tool that is based on a computationally tractable, algorithmically expressive, and mathematically aesthetic domain of linear algebra. Vercel works on abstracting out packet headers into standard basis vectors that are used to create a port-specific forwarding matrix$\mathcal {A}$, representing a set of packet headers/prefixes that a router forwards along a port. By equating this matrix$\mathcal {A}$and a vector b (that represents the set of all headers under consideration), we are able to apply least squares (which produces a column rank agnostic solution) to compute which headers are reachable at the destination. Reachability now simply means evaluating if vector b is in the column space of$\mathcal {A}$, which can efficiently be computed using least squares. Further, the use of vector representation and least squares opens new possibilities for understanding network behavior. For example, we are able to map rules, routing policies, what-if scenarios to the fundamental linear algebraic form,$\mathcal {A}x=b$, as well as determine how to configure forwarding tables appropriately. We show Vercel is faster than the state-of-art such as NetPlumber, Veriflow, APKeep, AP Verifier, when measured over diverse datasets. Vercel is almost as fast as Deltanet, when rules are verified in batches and provides better scalability, expressiveness and memory efficiency. A key highlight of Vercel is that while evaluating for reachability, the tool can incorporate intents, and transform these into auto-configurable table entries, implying a recommendation/correction system.

This paper proposes Trailnet, a deep reinforcement learning approach to predict the output port (of a router) for an IP packet based on its destination IP address. Trailnet attempts to replace the forwarding table at an IP router with a computational model. To optimally learn each router’s forwarding decisions, we propose to train the Artificial Neural Network (ANN) of Trailnet with value iteration and stochastic gradient descent. Through the value iteration algorithm, Trailnet estimates the cost of IP packet forwarding along different ports of a router and eventually selects a port that optimizes a cost function. We evaluate the generalization capability of the ANN on two sufficiently large service provider’s network topologies containing millions of IP addresses. Our evaluations show that Trailnet achieves high accuracy and fast inference time for predicting the correct output ports for incoming IP packets. Our results support the claim of replacing forwarding tables and distributed protocols (for computing shortest paths) with a computation model while operating at a high line rate in IP routers.