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

Detection and restoration times are oftenignored when modeling network reliability. In thispaper, we develop Markov Regenerative Reward Models(MRRM) to capture the effects of detection andrestoration phases of network recovery. States of the MRRMrepresent conditions of network resources, while statetransitions represent occurrences of failure, repair,detection, and restoration. Reward rates, assigned to states of the MRRM are computed based on aperformance model that accounts for contention. Wecompare our model with ones that ignore these parametersand show significant differences, in particular for transient measures.