Probe Scheduling for Efficient Detection of Silent Failures

TL;DR

This paper develops and analyzes efficient probe scheduling strategies for detecting silent failures in networks, balancing detection speed and resource use through novel stochastic and deterministic methods.

Contribution

It introduces a unified model for probe scheduling, provides efficient algorithms for memoryless schedules, and develops new deterministic schedulers with provable approximation guarantees.

Findings

Memoryless schedules can be efficiently optimized via convex or linear programming.

Deterministic schedules with bounded worst-case detection times are achievable with approximation guarantees.

Simulation results show significant improvements over previous methods.

Abstract

Most discovery systems for silent failures work in two phases: a continuous monitoring phase that detects presence of failures through probe packets and a localization phase that pinpoints the faulty element(s). This separation is important because localization requires significantly more resources than detection and should be initiated only when a fault is present. We focus on improving the efficiency of the detection phase, where the goal is to balance the overhead with the cost associated with longer failure detection times. We formulate a general model which unifies the treatment of probe scheduling mechanisms, stochastic or deterministic, and different cost objectives - minimizing average detection time (SUM) or worst-case detection time (MAX). We then focus on two classes of schedules. {\em Memoryless schedules} -- a subclass of stochastic schedules which is simple and suitable for distributed deployment. We show that the optimal memorlyess schedulers can be efficiently computed by convex programs (for SUM objectives) or linear programs (for MAX objectives), and surprisingly perhaps, are guaranteed to have expected detection times that are not too far off the (NP hard) stochastic optima. {\em Deterministic schedules} allow us to bound the maximum (rather than expected) cost of undetected faults, but like stochastic schedules, are NP hard to optimize. We develop novel efficient deterministic schedulers with provable approximation ratios. An extensive simulation study on real networks, demonstrates significant performance gains of our memoryless and deterministic schedulers over previous approaches. Our unified treatment also facilitates a clear comparison between different objectives and scheduling mechanisms.