{"id":166175,"date":"2014-04-01T00:00:00","date_gmt":"2014-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/adtributor-revenue-debugging-in-advertising-systems\/"},"modified":"2018-10-16T20:10:53","modified_gmt":"2018-10-17T03:10:53","slug":"adtributor-revenue-debugging-in-advertising-systems","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/adtributor-revenue-debugging-in-advertising-systems\/","title":{"rendered":"Adtributor: Revenue Debugging in Advertising Systems"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Advertising (ad) revenue plays a vital role in supporting free websites. When the revenue dips or increases sharply, ad system operators must find and fix the root-cause if actionable, for example, by optimizing infrastructure performance.  Such revenue debugging is analogous to diagnosis and root-cause analysis in the systems literature but is more general.  Failure of infrastructure elements is only one potential cause; a host of other dimensions (e.g., advertiser, device type) can be sources of potential causes. Further, the problem is complicated by derived measures such as costs-per-click that are also tracked along with revenue.<\/p>\n<p>Our paper takes the first systematic look at revenue debugging. Using the concepts of explanatory power, succinctness, and surprise, we propose a new multi-dimensional root cause algorithm for fundamental and derived measures of ad systems to identify the dimension mostly likely to blame.  Further, we implement the attribution algorithm and a visualization interface in a tool called the Adtributor to  help troubleshooters quickly identify potential causes. Based on several case studies on a very large ad system and extensive evaluation, we show that the Adtributor has an accuracy of over 95% and helps cut down troubleshooting time by an order of magnitude.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Advertising (ad) revenue plays a vital role in supporting free websites. When the revenue dips or increases sharply, ad system operators must find and fix the root-cause if actionable, for example, by optimizing infrastructure performance. Such revenue debugging is analogous to diagnosis and root-cause analysis in the systems literature but is more general. Failure of [&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":[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-166175","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"USENIX","msr_edition":"Symposium on Networked Systems Design and Implemenentation 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