Modern cloud services aim to find the middle ground between quality of service and operational cost efficiency by allocating resources if and only if these resources are needed by the customers. Unfortunately, most industrial demand-driven resource allocation approaches are reactive. Given that scaling mechanisms are not instantaneous, the reactive policy may introduce delays to latency-sensitive customer workloads and waste operational costs for cloud service providers. To solve this catch-22, we define the proactive resource allocation policy for Microsoft Azure Cognitive Search. In addition to the current resource demand, the proactive policy takes the typical resource usage patterns into account. We gained the following valuable insights from these patterns over several months of production workloads. One, 87% of the workload is stable due to continuous resource demand. Two, 90% of varying demand is predictable based on a few weeks of historical traces. Three, resources can be reclaimed 52% of the time due to extensive idle intervals of varying workload. Given the size and scope of our analysis, we believe that our approach applies to any latency-sensitive cloud service.<\/p>\n","protected":false},"excerpt":{"rendered":"
Modern cloud services aim to find the middle ground between quality of service and operational cost efficiency by allocating resources if and only if these resources are needed by the customers. Unfortunately, most industrial demand-driven resource allocation approaches are reactive. Given that scaling mechanisms are not instantaneous, the reactive policy may introduce delays to latency-sensitive […]<\/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],"msr-publication-type":[193715],"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-974064","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2023-9-5","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"SIGMOD Rec.","msr_volume":"52","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"3","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":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/sigmodrecord.org\/publications\/sigmodRecord\/2309\/pdfs\/08_Industry_Poppe.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Olga Poppe","user_id":40054,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Olga Poppe"},{"type":"text","value":"Pablo Castro","user_id":0,"rest_url":false},{"type":"text","value":"Willis Lang","user_id":0,"rest_url":false},{"type":"edited_text","value":"Jyoti Leeka","user_id":40066,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jyoti Leeka"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[723823],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":723823,"post_title":"Proactive Resource Allocation","post_name":"moneyball","post_type":"msr-project","post_date":"2021-04-24 17:47:08","post_modified":"2024-07-13 10:59:09","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/moneyball\/","post_excerpt":"Modern cloud services aim to allocate resources if and only if the customers use these resources to optimize quality of service and operational cost efficiency. Recent approaches have evolved from merely reactive policies to proactive decision making. These approaches leverage not only the current resource demand but also the predicted future demand to make more informed resource allocation decisions for each database. We define a proactive resource allocation policy and design a distributed infrastructure that…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/723823"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/974064","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/974064\/revisions"}],"predecessor-version":[{"id":1018716,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/974064\/revisions\/1018716"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=974064"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=974064"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=974064"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=974064"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=974064"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=974064"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=974064"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=974064"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=974064"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=974064"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=974064"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=974064"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=974064"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=974064"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=974064"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=974064"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}