@inproceedings{bhagwan2011deja,
author = {Bhagwan, Ranjita and Padmanabhan, Venkat and Carli, Lorenzo de and Puttaswamy, Krishna and Aggarwal, Bhavish},
title = {Deja vu: Fingerprinting Network Problems},
booktitle = {The 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2011)},
year = {2011},
month = {December},
abstract = {We ask the question: can network problems experienced by applications be identified based on symptoms contained in a network packet trace? An answer in the affirmative would open the doors to many opportunities, including nonintrusive monitoring of such problems on the network and matching a problem with past instances of the same problem. To this end, we present Deja vu, a tool to condense the manifestation of a network problem into a compact signature, which could then be used to match multiple instances of the same problem. Deja vu uses as input a network-level packet trace of an application’s communication and extracts from it a set of features. During the training phase, each application run is manually labeled as GOOD or BAD, depending on whether the run was successful or not. Deja vu then employs a novel learning technique to build a signature tree not only to distinguish between GOOD and BAD runs but to also sub-classify the BAD runs, revealing the different classes of failures. The novelty lies in performing the sub-classification without requiring any failure class-specific labels. We evaluate Deja vu in the context of the multiple web browsers in a corporate environment and an email application in a university environment, with promising results. The signature generated by Deja vu based on the limited GOOD/BAD labels is as effective as one generated using full-blown classification with knowledge of the actual problem types.},
publisher = {ACM SIGCOMM},
url = {http://approjects.co.za/?big=en-us/research/publication/deja-vu-fingerprinting-network-problems/},
edition = {The 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2011)},
}