Adaptive Statistical Signatures of Network Soft-Failures in User Devices

摘要

Efficient fault detection and characterization are crucial requirements for automated network diagnosis systems. In this paper, we present normalized statistical signatures (NSSs), a network 'soft-failure' characterization technique for user devices (UDs) based upon exhaustive sets of aggregated statistical features extracted from transmission control protocol (TCP) packet streams. TCP streams are collected on-demand (e.g. upon user complaint) with fixed data transfer limits to capture the artifacts resulting from UD faults. NSSs created using live network and testbed data are able to uniquely characterize many faults and offer insight into how various types of features are affected by the faults and networks. We then introduce the link adaptive signature estimation (LASE) technique to reduce the quantities of collected NSSs required for generalized diagnostic systems with variable link parameters. We create feature estimator functions using multivariate regression techniques to generate artificial NSSs, which are subsequently used to train machine learning systems that have robust generalization capabilities. Performance of a prototype fault classifier system based on NSSs shows that an overall detection accuracy of 98% can be achieved for eight types of faults in a live network environment. In this paper, we specifically focus on formulating the basic framework of NSS and LASE, and limit the analysis to wired networks. This work can later be extended to encompass more complex fixed and mobile wireless networking environments. We expect that the combination of NSSs and LASE can serve as the foundation of next-generation automated network diagnosis systems.