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Most discovery systems for silent failures work in two phases: a continuousmonitoring phase that detects presence of failures through probe packets and alocalization phase that pinpoints the faulty element(s). This separation isimportant because localization requires significantly more resources thandetection and should be initiated only when a fault is present.  We focus on improving the efficiency of the detection phase, where the goalis to balance the overhead with the cost associated with longer failuredetection times. We formulate a general model which unifies the treatment ofprobe scheduling mechanisms, stochastic or deterministic, and different costobjectives - minimizing average detection time (SUM) or worst-case detectiontime (MAX).  We then focus on two classes of schedules. {\em Memoryless schedules} -- asubclass of stochastic schedules which is simple and suitable for distributeddeployment. We show that the optimal memorlyess schedulers can be efficientlycomputed by convex programs (for SUM objectives) or linear programs (for MAXobjectives), and surprisingly perhaps, are guaranteed to have expecteddetection times that are not too far off the (NP hard) stochastic optima. {\emDeterministic schedules} allow us to bound the maximum (rather than expected)cost of undetected faults, but like stochastic schedules, are NP hard tooptimize. We develop novel efficient deterministic schedulers with provableapproximation ratios.  An extensive simulation study on real networks, demonstrates significantperformance gains of our memoryless and deterministic schedulers over previousapproaches. Our unified treatment also facilitates a clear comparison betweendifferent objectives and scheduling mechanisms.

Probe Scheduling for Efficient Detection of Silent Failures