{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/007-democratically-finding-the-cause-of","title":"007: Democratically Finding The Cause of Packet Drops","arxiv_id":"1802.07222","date":"2018-02-20","proceeding":null,"authors":["Behnaz Arzani","Selim Ciraci","Luiz Chamon","Yibo Zhu","Hingqiang Liu","Jitu Padhye","Boon Thau Loo","Geoff Outhred"],"abstract":"Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur. We introduce 007, a lightweight, always-on diagnosis application that can find problematic links and also pinpoint problems for each TCP connection. 007 is completely contained within the end host. During its two month deployment in a tier-1 datacenter, it detected every problem found by previously deployed monitoring tools while also finding the sources of other problems previously undetected.","url_abs":"http://arxiv.org/abs/1802.07222v1","url_pdf":"http://arxiv.org/pdf/1802.07222v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"007-democratically-finding-the-cause-of","repo_url":"https://github.com/behnazak/Vigil-007SourceCode","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on-1","task":"Anomaly Detection In Surveillance Videos","dataset":"ShanghaiTech Weakly Supervised","model":"Learning Causal Temporal Relation and Feature Discrimination for Anomaly Detection","rank_in_archive_order":3,"of":12,"metrics":{"AUC-ROC":"97.48"},"uses_additional_data":false},{"leaderboard":"/sota/continuous-control-on-deepmind-cheetah-run","task":"Continuous Control","dataset":"DeepMind Cheetah Run (Images)","model":"PlaNet","rank_in_archive_order":4,"of":4,"metrics":{"Return":"650"},"uses_additional_data":false},{"leaderboard":"/sota/language-modelling-on-100-sleep-nights-of-8","task":"Language Modelling","dataset":"100 sleep nights of 8 caregivers","model":"Gpt3","rank_in_archive_order":1,"of":1,"metrics":{"10%":"1"},"uses_additional_data":true},{"leaderboard":"/sota/node-classification-on-cora-60-20-20-random","task":"Node Classification","dataset":"Cora (60%/20%/20% random splits)","model":"GCN+JK","rank_in_archive_order":23,"of":33,"metrics":{"1:1 Accuracy":"86.90 ± 1.51"},"uses_additional_data":false},{"leaderboard":"/sota/sign-language-recognition-on-rwth-phoenix","task":"Sign Language Recognition","dataset":"RWTH-PHOENIX-Weather 2014","model":"SLRGAN","rank_in_archive_order":16,"of":22,"metrics":{"Word Error Rate (WER)":"23.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}