Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability

Abstract

Network reliability is very important for the decision support information. Monte Carlo Simulation (MCS) is one of the optimal algorithms to estimate the network reliability for different kinds of network configuration. The traditional reliability estimation requires the information of all Minimal Paths (MPs) or Minimal Cuts (MCs). However, finding all MPs/MCs is extremely computationally expensive. This paper has compared and analyzed three Monte Carlo Simulation (MCS) methods for estimating the two-terminal network reliability of a binary-state network: (1) MCS1 simulates the network reliability in terms of known MPs, (2) MCS2 estimates the network reliability in terms of known MCs; and (3) CAMCS (based on cellular automata, CA) estimates the network reliability directly without knowing any information of MPs or MCs. Our simulation results show that the direct estimation without knowing any information of MPs or MCs can speedup about 185 times when compared with other traditional approaches which require MPs or MCs information.