As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data center\u2019s operating costs. In general, aggressively aggregating tenants on each server reduces the operating costs but degrades performance for the tenants, and vice versa. In this paper, we present a framework that takes as input the tenant workloads, their performance SLOs, and the server hardware that is available to the DaaS provider, and outputs a cost-effective recipe that specifies how much hardware to provision and how to schedule the tenants on each hardware resource. We evaluate our method and show that it produces effective solutions that can reduce the costs for the DaaS provider while meeting performance goals.<\/p>\n","protected":false},"excerpt":{"rendered":"
As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data […]<\/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":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"6","msr_journal":"IEEE Transactions on Knowledge and Data Engineering","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"1447","msr_page_range_end":"1463","msr_series":"","msr_volume":"26","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"2067080594","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2014-5-31","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13561],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[247555,246691,251983,248884,251548,252121],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-724990","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-locale-en_us","msr-field-of-study-cloud-computing","msr-field-of-study-computer-science","msr-field-of-study-data-center","msr-field-of-study-database","msr-field-of-study-relational-database","msr-field-of-study-service-level-objective"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2014-5-31","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"IEEE Transactions on Knowledge and Data Engineering","msr_volume":"26","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"6","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":"doi","viewUrl":"false","id":"false","title":"10.1109\/TKDE.2013.74","label_id":"243106","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dblp.uni-trier.de\/db\/journals\/tkde\/tkde26.html#LangSPK14","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/doi.org\/10.1109\/TKDE.2013.74","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/ieeexplore.ieee.org\/document\/6513229\/","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.computer.org\/csdl\/trans\/tk\/2014\/06\/06513229.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Willis Lang","user_id":0,"rest_url":false},{"type":"text","value":"Srinath Shankar","user_id":0,"rest_url":false},{"type":"text","value":"Jignesh M. 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