{"id":154709,"date":"2005-09-01T00:00:00","date_gmt":"2005-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/detecting-application-level-failures-in-component-based-internet-services\/"},"modified":"2018-10-16T20:16:14","modified_gmt":"2018-10-17T03:16:14","slug":"detecting-application-level-failures-in-component-based-internet-services","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/detecting-application-level-failures-in-component-based-internet-services\/","title":{"rendered":"Detecting Application-Level Failures in Component-based Internet Services"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Most Internet services (e-commerce, search engines, etc.) suffer faults. Quickly detecting these faults can be the largest bottleneck in improving availability of the system. We present Pinpoint, a methodology for automatic fault detection in Internet services by (1) observing low-level, internal structural behaviors of the service; (2) modeling the majority behavior of the system as correct; and (3) detecting anomalies in these behaviors as possible symptoms of failures. Without requiring any a priori application-specific information, Pinpoint correctly detected 89-96% of major failures in our experiments, as compared to 20-70% detected by current application-generic techniques.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most Internet services (e-commerce, search engines, etc.) suffer faults. Quickly detecting these faults can be the largest bottleneck in improving availability of the system. We present Pinpoint, a methodology for automatic fault detection in Internet services by (1) observing low-level, internal structural behaviors of the service; (2) modeling the majority behavior of the system as [&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":"","msr-author-ordering":null,"msr_publishername":"IEEE Computer Society","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"IEEE Transactions on Neural Networks: Special Issue on Adaptive Learning Systems in Communication Networks","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"IEEE Transactions on Neural Networks: Special Issue on Adaptive Learning Systems in Communication Networks","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 IEEE. Reprinted from IEEE Computer Society. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. 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