Efficient estimation of distance-dependent metrics in edge-failing networks

Abstract

This article introduces a method for estimating performability metrics built upon non-binary network states, determined by the hop distances between distinguished nodes. The estimation is performed by a Monte Carlo simulation where the sampling space is reduced using edge sets known as d-pathsets and d-cutsets. Numerical experiments over two mesh-like networks are presented. They show significant efficiency improvements relative to the crude Monte Carlo method, in particular as link failures become rarer events, which is usually the case in most real communication networks.