{"id":182048,"date":"2009-05-05T00:00:00","date_gmt":"2009-10-31T09:19:10","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/automatic-workload-evaluation-awe-predicting-web-2-0-workload-behavior\/"},"modified":"2016-09-09T09:45:10","modified_gmt":"2016-09-09T16:45:10","slug":"automatic-workload-evaluation-awe-predicting-web-2-0-workload-behavior","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/automatic-workload-evaluation-awe-predicting-web-2-0-workload-behavior\/","title":{"rendered":"Automatic Workload Evaluation (AWE):  Predicting Web 2.0 Workload Behavior"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The aim of this project is to use statistical machine learning to predict a system&#8217;s performance and resource utilization under changes to the workload or underlying hardware.  This could be useful in many scenarios.  For instance, in the web service domain, when a company wants to promote a feature of their application and thereby shift the distribution of requests towards a certain type, they would like to have a sense for the resulting performance.  Also, when it comes time to upgrade their servers, they would like to predict the system&#8217;s behavior on the new hardware.  This work is inspired by a study done by Ganapathi et al. (ICDE &#8217;09) in which this approach was utilized for predicting query runtime and resource consumption; the contribution of this work is the application of the predictive framework to the web service domain via analysis of a new Web 2.0 social networking benchmark application called Cloudstone.  In previous experiments, we validated that our representation of the input allowed us to recreate the CPU utilization of the system; in this phase of the project, we focus on predicting the performance and resource utilization of a given workload.  In this talk, I will describe our framework and present experimental results.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The aim of this project is to use statistical machine learning to predict a system&#8217;s performance and resource utilization under changes to the workload or underlying hardware. This could be useful in many scenarios. For instance, in the web service domain, when a company wants to promote a feature of their application and thereby shift [&hellip;]<\/p>\n","protected":false},"featured_media":194428,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-182048","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/8q5yR6mbQsA","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182048","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182048\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/194428"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=182048"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=182048"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=182048"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=182048"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=182048"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=182048"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=182048"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=182048"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=182048"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=182048"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}