FEAL: A source routing Framework for Efficient Anomaly Localization

Abstract

Source routing represents a good opportunity to enhance monitoring solutions, particularly probing techniques. This technique allows deploying customized probing schemes to fulfill different monitoring needs like troubleshooting or Service Level Agreement (SLA) supervision. In this context, the use of probing cycles is a promising monitoring method. The deployment of such probing schemes becomes easier thanks to source routing since it allows constraining the traffic to follow specific paths.In this paper we propose the FEAL monitoring framework (Framework for Efficient Anomaly Localization) based on source routing probing cycles. The framework is mainly composed of two parts: the "Probing Cycles" and the "Anomaly Detection" modules. The first one defines the probing strategy by deploying the needed monitors and finding the probing cycles to cover the network topology. The "Anomaly Detection" module is based on our previously proposed statistical algorithm for the inference of link metrics named ESA (Evolutionary Sampling Algorithm) [1], here extended to more general classes of metrics. We prototype and evaluate the FEAL framework with a P4 implementation of source routing over a Mininet emulator. The results show that our framework detects and localizes efficiently the failure points in the network.

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“…The former identify a shortest-path while the latter identify a link segment. The use of nodes-SIDs is feasible; however it may lead to an ambiguous definition of the probing cycle and accordingly introduce a bias in the measurement process [16]. In fact, when using nodes-SIDs to identify a path between a source s and a destination d and due to the load balancing mechanism deployed, we will not be sure which path the traffic followed exactly.…”

“…More recently, M.Rahali and al. [16] focused on the same problem we consider here. They propose a Greedy solution to which we compare our learning based method.…”

“…To summarize, to determine the optimal set of monitors we Algorithm 1 Compute Cycles whose length less or equal to k [16] Input: a graph G = (V, L), maximum length of cycles k Output: The set of cycles C…”