The Effect of Detection and Restoration Times for Error Recovery in Communication Networks

Abstract

Detection and restoration times are often ignored when modeling network reliability. In this paper, we develop Markov Regenerative Reward Models (MRRM) to capture the effects of detection and restoration phases of network recovery. States of the MRRM represent conditions of network resources, while state transitions represent occurrences of failure, repair, detection, and restoration. Reward rates, assigned to states of the MRRM are computed based on a performance model that accounts for contention. We compare our model with ones that ignore these parameters and show significant differences, in particular for transient measures.