Developers deploying web applications in the cloud often need to determine how changes such as service tiers or runtime loads may affect user-perceived page load time. We devise and evaluate a systematic methodology for exploring such “what-if” questions at the time a web application is deployed. Given a website, a web request, and \u201cwhatif\u201d scenario, with a hypothetical configuration and runtime condition, our methodology, embedded in a system called WebPerf, can estimate a distribution of end-to-end response times for the request under the \u201cwhat-if\u201d scenario. WebPerf makes three contributions: (1) automated instrumentation of web sites written with increasingly popular task parallel libraries, to capture causal call dependencies of various computation and asynchronous I\/O calls; (2) an algorithm to use the call dependencies, together with online- and offlineprofiled models of various I\/O calls to estimate a distribution of end-to-end latency of the request; and (3) an algorithm to optimize modeling errors by deciding how many measurements to take within a limited time. We have implemented WebPerf for Microsoft Azure. Our experiments with five real websites and seven scenarios show that the median error of WebPerf\u2019s estimation is within 7% for all applications and scenarios.<\/p>\n","protected":false},"excerpt":{"rendered":"
Developers deploying web applications in the cloud often need to determine how changes such as service tiers or runtime loads may affect user-perceived page load time. We devise and evaluate a systematic methodology for exploring such “what-if” questions at the time a web application is deployed. Given a website, a web request, and \u201cwhatif\u201d scenario, […]<\/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-247259","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"","msr_affiliation":"","msr_published_date":"2016-7-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"247262","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/07\/WebPerfCameraReady.pdf","id":"247262","title":"WebPerfCameraReady","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Yurong Jiang","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Lenin Ravindranath Sivalingam","user_id":32645,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Lenin Ravindranath Sivalingam"},{"type":"user_nicename","value":"Suman Nath","user_id":33753,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Suman Nath"},{"type":"text","value":"Ramesh Govindan","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[],"msr_project":[554055],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":554055,"post_title":"Torch","post_name":"torch","post_type":"msr-project","post_date":"2018-11-28 15:34:02","post_modified":"2022-12-02 14:18:51","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/torch\/","post_excerpt":"We aim to develop practical tools and techniques that can help cloud developers adequately debug, test, configure, and monitor their systems. 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