{"id":167032,"date":"2014-08-01T00:00:00","date_gmt":"2014-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/scalable-near-real-time-failure-localization-of-data-center-networks\/"},"modified":"2018-10-16T21:30:24","modified_gmt":"2018-10-17T04:30:24","slug":"scalable-near-real-time-failure-localization-of-data-center-networks","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/scalable-near-real-time-failure-localization-of-data-center-networks\/","title":{"rendered":"Scalable Near Real-Time Failure Localization of Data Center Networks"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Large-scale data center networks are complex|comprising several thousand network devices and several hundred thousand links|and form the critical infrastructure upon which all higher-level services depend on. Despite the built-in redundancy in data center networks, performance issues and device or link failures in the network can lead to user-perceived service interruptions. Therefore, determining and localizing user-impacting availability and performance issues in the network in near real time is crucial. Traditionally, both passive and active monitoring approaches have been used for failure localization. However, data from passive monitoring is often too noisy and does not effectively capture silent or gray failures, whereas active monitoring is potent in detecting faults but limited in its ability to isolate the exact fault location depending on its scale and granularity.<\/p>\n<p>Our key idea is to use statistical data mining techniques on large-scale active monitoring data to determine a ranked list of suspect causes, which we refine with passive monitoring signals. In particular, we compute a failure probability for devices and links in near real time using data from active monitoring, and look for statistically significant increases in the failure probability. We also correlate the probabilistic output with other failure signals from passive monitoring to increase the confidence of the probabilistic analysis. We have implemented our approach in the Windows Azure production environment and have validated its effectiveness in terms of localization accuracy, precision, and time to localization using known network incidents over the past three months. The correlated ranked list of devices and links is surfaced as a report that is used by network operators to investigate current issues and identify probable root causes.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Large-scale data center networks are complex|comprising several thousand network devices and several hundred thousand links|and form the critical infrastructure upon which all higher-level services depend on. Despite the built-in redundancy in data center networks, performance issues and device or link failures in the network can lead to user-perceived service interruptions. Therefore, determining and localizing user-impacting [&hellip;]<\/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":[13563,13547],"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-167032","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM - Association for Computing Machinery","msr_edition":"in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge 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