Dynamic Network Delay Cartography

TL;DR

This paper introduces a spatio-temporal Kalman filtering method for constructing comprehensive network delay maps efficiently, using minimal measurements and optimizing path selection to improve delay tracking and prediction accuracy.

Contribution

It presents a novel framework combining Kalman filtering with online path selection based on submodularity for efficient network delay cartography.

Findings

Outperforms existing methods on real-world datasets

Efficiently tracks and predicts network delays with limited measurements

Provides an optimal linear predictor for delay estimation

Abstract

Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network is however challenging and wasteful of resources. The present paper advocates a spatio-temporal Kalman filtering approach to construct network-wide delay maps using measurements on only a few paths. The proposed network cartography framework allows efficient tracking and prediction of delays by relying on both topological as well as historical data. Optimal paths for delay measurement are selected in an online fashion by leveraging the notion of submodularity. The resulting predictor is optimal in the class of linear predictors, and outperforms competing alternatives on real-world datasets.