Local Fast Rerouting with Low Congestion: A Randomized Approach

TL;DR

This paper introduces randomized local fast rerouting algorithms that enhance network resilience against multiple link failures while maintaining low congestion, outperforming deterministic methods with high probability.

Contribution

It presents three simple, provably guaranteed algorithms that improve resilience-load tradeoffs in highly connected networks under multiple failures.

Findings

Algorithms outperform deterministic approaches with high probability.

Resilience against multiple link failures is significantly improved.

Low congestion is maintained on failover paths.

Abstract

Most modern communication networks include fast rerouting mechanisms, implemented entirely in the data plane, to quickly recover connectivity after link failures. By relying on local failure information only, these data plane mechanisms provide very fast reaction times, but at the same time introduce an algorithmic challenge in case of multiple link failures: failover routes need to be robust to additional but locally unknown failures downstream. This paper presents local fast rerouting algorithms which not only provide a high degree of resilience against multiple link failures, but also ensure a low congestion on the resulting failover paths. We consider a randomized approach and focus on networks which are highly connected before the failures occur. Our main contribution are three simple algorithms which come with provable guarantees and provide interesting resilience-load tradeoffs, significantly outperforming any deterministic fast rerouting algorithm with high probability.