{"id":162136,"date":"2011-12-01T00:00:00","date_gmt":"2011-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/deja-vu-fingerprinting-network-problems\/"},"modified":"2018-10-16T20:10:57","modified_gmt":"2018-10-17T03:10:57","slug":"deja-vu-fingerprinting-network-problems","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/deja-vu-fingerprinting-network-problems\/","title":{"rendered":"Deja vu: Fingerprinting Network Problems"},"content":{"rendered":"
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\u2019s 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.<\/p>\n<\/div>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-162136","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM SIGCOMM","msr_edition":"The 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2011)","msr_affiliation":"","msr_published_date":"2011-12-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"The 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2011)","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"206263","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"dejavu.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/dejavu.pdf","id":206263,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":206263,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/dejavu.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"bhagwan","user_id":31217,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=bhagwan"},{"type":"user_nicename","value":"padmanab","user_id":33180,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=padmanab"},{"type":"text","value":"Lorenzo de Carli","user_id":0,"rest_url":false},{"type":"text","value":"Krishna Puttaswamy","user_id":0,"rest_url":false},{"type":"user_nicename","value":"bhavisha","user_id":31220,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=bhavisha"}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[144725,144939],"msr_project":[170175],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170175,"post_title":"NetPrints","post_name":"netprints","post_type":"msr-project","post_date":"2008-12-23 21:47:14","post_modified":"2017-06-01 15:57:29","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/netprints\/","post_excerpt":"Diagnosing Home Network Misconfigurations using Shared Knowledge The Problem Today's home network is typically a collection of several devices and applications. 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