[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86319-en":3,"doc-seo-86319-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86319,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","AutoSLO Practical Latency SLOs on Cloud Data Warehouses Extended Version","AutoSLO is a latency-SLO-aware workload management framework for multi-cluster cloud data warehouses. It addresses the challenge that decoupled compute and storage enable workload isolation but require continuous scaling to avoid wasted resources or SLO violations. AutoSLO coordinates actions across three timescales: a periodic Policy Tuner with history-derived forecasts, an SLO-aware reactive Autoscaler for cluster set changes, and an online Query Router using a concurrency-aware latency predictor. On Redbench, AutoSLO meets latency SLOs of varying strictness while reducing mean cost by 26.4%.","AutoSLO: Practical Latency SLOs on Cloud Data Warehouses –  \nExtended Version  \nMarkos Markakis  \nMIT CSAIL Cambridge, MA, USA [markakis@mit.edu](markakis@mit.edu)  \nTim Kraska  \nMIT CSAIL Cambridge, MA, USA [kraska@mit.edu](kraska@mit.edu)  \nABSTRACT  \nModern cloud data warehouses decouple compute from storage, making it easy for organizations to access the same underlying data with multiple compute clusters. This flexibility is often used for performance isolation among diverse workloads, so that each workload meets its latency service-level objective (SLO) more reliably. For example, interactive dashboards, ad hoc analysis, and batch jobs can each run on separate clusters. However, this dedicated-cluster approach requires each compute cluster to be continuously scaled to adapt to workload evolution, with over-provisioning wasting resources and under-provisioning risking SLO violations.  \nWe present AutoSLO, a latency-SLO-aware workload management framework for multi-cluster cloud data warehouses. AutoSLO operates across three timescales through three key components. First, a periodic Policy Tuner plans proactive cluster scaling actions and tunes configuration parameters, using simulations of history-derived workload forecasts. Second, an SLO-aware reactive Autoscaler adjusts the active cluster set when recent workload behavior deviates from the forecast. Third, an online Query Router reacts to live load when placing each query, using a concurrencyaware latency predictor to avoid SLO violations.  \nOn realistic Redbench workloads, AutoSLO successfully meets latency SLOs of varying strictness, reducing cost by a mean of 26.4% compared to the per-scenario next-best baseline. Component-level evaluations show that the Query Router and Autoscaler respectively reduce SLO violation rates by a mean of 47. 8% and 93. 7%, relative to their corresponding alternatives. Finally, we show that the Policy Tuner can reduce the SLO violation rate by a mean of 44. 6% using a single day of workload history, and that each component is efficient given its intended operating timescale.  \n1 INTRODUCTION  \nThe Problem. Modern cloud data warehouses such as Amazon Redshift Serverless [3] and Snowflake [52] decouple compute from storage, enabling multiple compute clusters to access the same underlying data. For example, multiple Amazon Redshift Serverless workgroups can access the same data through Datashares [5], while Snowflake natively supports multi-cluster warehouses [50] .  \nCustomers have embraced this feature [55], because it enables coarse-grained performance isolation by allowing the separation of workloads with different latency service-level objectives (SLOs) . For example, interactive dashboards may need to respond within a few seconds, nightly ETL workloads may need to finish before business hours, and data-science workloads may tolerate some additional latency as long as costs are kept bounded.  \nHowever, even if workloads are each assigned to a separate cluster, current systems do not allow SLOs to be specified directly. This  \nmeans that administrators have to experiment with the cluster’s settings (e.g. number of nodes or qualitative price-performance hint [39]) until the desired latency SLO is met. Even after such tuning, there is no control over what happens as queries within each workload may interfere with one another. As a recent work by the Amazon Redshift team notes, “large ad-hoc queries can have a severe negative impact on the overall performance of the cluster, since they can occupy compute resources and cause cache thrashing.” [39] In this work, we take a different approach: we treat latency SLOs as first-class inputs and use the mechanisms exposed by cloud data warehouses to meet them cost-efficiently. The goal is not cross  \nworkload performance isolation for its own sake, but rather meeting the SLO of each individual query, while minimizing infrastructure cost. This requires deciding which clusters to use, when to scale t","cbCaichbousdiGL2","https://ap.wps.com/l/cbCaichbousdiGL2","pdf",1508368,5,1,16,"English","en",105,"# Abstract\n# Introduction\n## The Problem\n## What Success Looks Like","[{\"question\":\"What problem does AutoSLO address in multi-cluster cloud data warehouses?\",\"answer\":\"AutoSLO targets the need to continuously scale compute clusters for latency SLO compliance when workloads are isolated across clusters. Dedicated clusters can waste resources if over-provisioned and risk violations if under-provisioned.\"},{\"question\":\"How does AutoSLO manage latency SLOs across different timescales?\",\"answer\":\"It uses three coordinated components: a periodic Policy Tuner for proactive planning, an SLO-aware reactive Autoscaler to adjust the active cluster set when behavior deviates from forecasts, and an online Query Router to place each incoming query using a concurrency-aware latency predictor.\"},{\"question\":\"What performance improvements does AutoSLO achieve compared to baselines?\",\"answer\":\"On realistic Redbench workloads, AutoSLO meets latency SLOs with a mean cost reduction of 26.4% versus a per-scenario next-best baseline. Component evaluations report SLO violation rate reductions of 47.8% for the Query Router and 93.7% for the Autoscaler relative to their alternatives.\"}]",1784210458,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"autoslo-practical-latency-slos-on-cloud-data-warehouses-extended-version","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/autoslo-practical-latency-slos-on-cloud-data-warehouses-extended-version/86319/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does AutoSLO address in multi-cluster cloud data warehouses?","Question",{"text":76,"@type":77},"AutoSLO targets the need to continuously scale compute clusters for latency SLO compliance when workloads are isolated across clusters. Dedicated clusters can waste resources if over-provisioned and risk violations if under-provisioned.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does AutoSLO manage latency SLOs across different timescales?",{"text":81,"@type":77},"It uses three coordinated components: a periodic Policy Tuner for proactive planning, an SLO-aware reactive Autoscaler to adjust the active cluster set when behavior deviates from forecasts, and an online Query Router to place each incoming query using a concurrency-aware latency predictor.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance improvements does AutoSLO achieve compared to baselines?",{"text":85,"@type":77},"On realistic Redbench workloads, AutoSLO meets latency SLOs with a mean cost reduction of 26.4% versus a per-scenario next-best baseline. Component evaluations report SLO violation rate reductions of 47.8% for the Query Router and 93.7% for the Autoscaler relative to their alternatives.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":20,"slug":137},19,"General","general"]