Network reliability estimation using the tree cut and merge algorithm with importance sampling

It is well known that the exact calculation of network reliability is a NP-complete problem and that for large networks estimating the reliability using simulation techniques becomes attractive. For highly reliable networks, a Monte Carlo scheme called the Merge Process is one of the best performing algorithms, but with a relatively high computational cost per sample. The authors previously proposed a hybrid Monte Carlo scheme called the Tree Cut and Merge algorithm which can improve simulation performance by over seven orders of magnitude in some heterogeneous networks. In homogeneous networks, however, the performance of the algorithm may degrade. In this paper, we first analyse the Tree Cut and Merge algorithm and explain why it does not perform well in some networks. Then a modification is proposed that subdivides the problem into smaller problems and introduces the Importance Sampling technique to the simulation process. The modified algorithm addresses the slow convergence problem in those hard cases while keeping the performance improvement in heterogeneous networks. Experiments and results are presented with some discussions.