{"id":1041513,"date":"2024-06-11T09:00:00","date_gmt":"2024-06-11T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/sibyl-a-machine-learning-based-framework-for-forecasting-dynamic-workloads\/"},"modified":"2024-06-19T10:58:59","modified_gmt":"2024-06-19T17:58:59","slug":"sibyl-a-machine-learning-based-framework-for-forecasting-dynamic-workloads","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/sibyl-a-machine-learning-based-framework-for-forecasting-dynamic-workloads\/","title":{"rendered":"SIBYL: A machine learning-based framework for forecasting dynamic workloads"},"content":{"rendered":"\n<p class=\"has-text-align-center\"><em><em><strong>This paper was presented at the <\/strong><\/em><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/2024.sigmod.org\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><em>ACM SIGMOD\/Principles of Database Systems Conference<\/em><\/strong><span class=\"sr-only\"> (opens in new tab)<\/span><\/a><em><strong> (SIGMOD\/PODS 2024), the premier forum on large-scale data management and databases.<\/strong><\/em><\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"788\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1.png\" alt=\"SIGMOD\/PODS 2024 logo to the left of the first page of accepted paper, \"SIBYL: Forecasting Time-Evolving Query Workloads\"\" class=\"wp-image-1041537\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1.png 1400w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-300x169.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-1024x576.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-768x432.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-1066x600.png 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-655x368.png 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-240x135.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-640x360.png 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-960x540.png 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-1280x720.png 1280w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><\/figure>\n\n\n\n<p>In today&#8217;s fast-paced digital landscape, data analysts are increasingly dependent on analytics dashboards to monitor customer engagement and app performance. However, as data volumes increase, these dashboards can slow down, leading to delays and inefficiencies. One solution is to use software designed to optimize how data is physically stored and retrieved, but the challenge remains in anticipating the specific queries analysts will run, a task complicated by the dynamic nature of modern workloads.<\/p>\n\n\n\n<p>In our paper, \u201c<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/sibyl-forecasting-time-evolving-query-workloads\/\">SIBYL: Forecasting Time-Evolving Query Workloads<\/a>,\u201d presented at SIGMOD\/PODS 2024, we introduce a machine learning-based framework designed to accurately predict queries in dynamic environments. This innovation allows traditional optimization tools, typically meant for static settings, to seamlessly adapt to changing workloads, ensuring consistent high performance as query demands evolve.<\/p>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"956148\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Microsoft Research Podcast<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/ai-frontiers-models-and-systems-with-ece-kamar\/\" aria-label=\"AI Frontiers: Models and Systems with Ece Kamar\" data-bi-cN=\"AI Frontiers: Models and Systems with Ece Kamar\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/04\/ece-podcast-_Topic_podcast-2023Mmm_hero_1400x788_16-9.jpg\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">AI Frontiers: Models and Systems with Ece Kamar<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p class=\"large\">Ece Kamar explores short-term mitigation techniques to make these models viable components of the AI systems that give them purpose and shares the long-term research questions that will help maximize their value.\u00a0<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button\">\n\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/ai-frontiers-models-and-systems-with-ece-kamar\/\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" aria-label=\"Listen now\" data-bi-cN=\"AI Frontiers: Models and Systems with Ece Kamar\" target=\"_blank\">\n\t\t\t\t\t\t\tListen now\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span>\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<h2 class=\"wp-block-heading\" id=\"sibyl-s-design-and-features\">SIBYL\u2019s design and features<\/h2>\n\n\n\n<p>SIBYL\u2019s framework is informed by studies of real-world workloads, which show that most are dynamic but follow predicable patterns. We identified the following recurring patterns in how parameters change over time:<\/p>\n\n\n\n<ul>\n<li><strong>Trending<\/strong>: Queries that increase, decrease, or remain steady over time.<\/li>\n\n\n\n<li><strong>Periodic<\/strong>: Queries that occur at regular intervals, such as hourly or daily.<\/li>\n\n\n\n<li><strong>Combination<\/strong>: A mix of trending and periodic patterns.<\/li>\n\n\n\n<li><strong>Random<\/strong>: Queries with unpredictable patterns.<\/li>\n<\/ul>\n\n\n\n<p>These insights, illustrated in Figure 1, form the basis of SIBYL\u2019s ability to forecast query workloads, enabling databases to maintain peak efficiency even as usage patterns shift.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"1295\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure2.jpg\" alt=\"A figure illustrating the analysis of how parameter changes with query arrival times, identifying four common patterns. The Y-axis represents the query arrival time and the X-axis shows the parameter values. Section (a) shows the trending pattern, which includes increasing, decreasing trends. Section (b) displays the periodic pattern, characterized by a regular pattern with fixed intervals such as hourly, daily, or weekly. Section (c) combines the trending and periodic patterns, while section (d) represents the random pattern, indicating no regular or predictable pattern. \" class=\"wp-image-1041600\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure2.jpg 1400w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure2-300x278.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure2-1024x947.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure2-768x710.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure2-195x180.jpg 195w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><figcaption class=\"wp-element-caption\">Figure 1. We studied the changing patterns and predictability of database queries by analyzing two weeks\u2019 worth of anonymized data from Microsoft\u2019s telemetry system, which guides decision-making for Microsoft products and services.<\/figcaption><\/figure>\n\n\n\n<p>SIBYL uses machine learning to analyze historical data and parameters to predict queries and arrival times. SIBYL\u2019s architecture, illustrated in Figure 2, operates in three phases:<\/p>\n\n\n\n<ul>\n<li><strong>Training<\/strong>: It uses historical query logs and arrival times to build machine learning models.<\/li>\n\n\n\n<li><strong>Forecasting<\/strong>: It employs pretrained models to predict future queries and their timing.<\/li>\n\n\n\n<li><strong>Incremental fine-tuning<\/strong>: It continuously adapts to new workload patterns through an efficient feedback loop.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"639\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Overview_SIBYL.jpg\" alt=\"The figure shows SIBYL\u2019s three phases. The first phase is a training phase: it featurizes the past queries and their arrival time, and trains ML models from scratch. The second phase is forecasting phase: it continuously receives recent queries from the workload traces and employs the pre-trained ML models from the training phase to predict the queries within the next time interval along with their expected arrival time. The last phase is the Incremental fine-tuning, it monitors model accuracy and detects workload shifts (e.g., new types of queries emerging in the workload) via a feedback loop. It adjusts its models efficiently by fine-tuning incrementally on the shifted workload, without retraining from scratch. \" class=\"wp-image-1041588\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Overview_SIBYL.jpg 1400w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Overview_SIBYL-300x137.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Overview_SIBYL-1024x467.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Overview_SIBYL-768x351.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Overview_SIBYL-240x110.jpg 240w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><figcaption class=\"wp-element-caption\">Figure 2. An overview of SIBYL\u2019s architecture.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"challenges-and-innovations-in-designing-a-forecasting-framework\">Challenges and innovations in designing a forecasting framework<\/h2>\n\n\n\n<p>Designing an effective forecasting framework is challenging, particularly in managing the varying number of queries and the complexity of creating separate models for each type of query. SIBYL addresses these by grouping high-volume queries and clustering low-volume ones, supporting scalability and efficiency. As demonstrated in Figure 3, SIBYL consistently outperforms other forecasting models, maintaining accuracy over different time intervals and proving its effectiveness in dynamic workloads.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"646\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure3.jpg\" alt=\"The figure presents a comprehensive comparison of four forecasting models across three different workloads: Telemetry, SCOPE, and BusTracker, and Sales dataset. The models compared are History-Based, Random Forest, Vanilla LSTM, and Sibyl-LSTMs. These models are evaluated based on three metrics: Recall, Precision, and F-1 Score. Each metric is represented in a separate column, while the workloads are organized in rows. The evaluation is done over different forecast intervals: 1 Hour, 6 Hours, 12 Hours, and 1 Day. \n\nSibyl-LSTMs surpasses other forecasting models and maintains stable accuracy across various time intervals settings. Vanilla LSTM and Random Forecast perform poorly on the Sales workload, which has more outliers and more unstable patterns. For Telemetry workload, the history-based method performs well with the 12-hour interval due to the workload\u2019s recurrent queries that have the same parameter values within a day (between the past 12-hour window and the future 12-hour window). But this method is ineffective with the one-day interval, as many query parameter values change when crossing the day boundary. The history-based method yields unsatisfactory results for the other three workloads that exhibit more rapid and intricate evolution and involve time-related parameters that operate on a finer time scale. Therefore, it is imperative to use an ML-based forecasting model to handle the evolving workload. \" class=\"wp-image-1041582\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure3.jpg 1400w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure3-300x138.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure3-1024x473.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure3-768x354.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/sibyl_blog_figure3-240x111.jpg 240w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><figcaption class=\"wp-element-caption\">Figure 3. SIBYL-LSTM&#8217;s accuracy compared with other models in forecasting queries for the next time interval.<\/figcaption><\/figure>\n\n\n\n<p>SIBYL adapts to changes in workload patterns by continuously learning, retaining high accuracy with minimal adjustments. As shown in Figure 4, the model reaches 95% accuracy after fine-tuning in just 6.4 seconds, nearly matching its initial accuracy of 95.4%.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"778\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Sibyl_blog_figure4.jpg\" alt=\"The figure consists of two parts a and b.  (a) depicts a pattern change of a parameter in the Telemetry workload. The Y-axis represents the query arrival time and the X-axis shows the parameter values. The shift in the patten starts from May 13 (highlighted in light blue), which Sibyl detects by observing the decline in accuracy. The model accuracy on the shifted pattern is 51.9%, which falls below the threshold \ud835\udefc = 75%, triggering model fine-tuning.  Figure 11 (b) shows that Sibyl fine-tunes the Sibyl-LSTMs by incrementally training on newly observed data, rather than training from scratch. The Y-axis represents recall, and the X-axis shows the number of epochs. Th figure demonstrates that the model converges in just two epochs, taking 6.4 seconds of overhead, and improves accuracy to 95.0%, which is close to the pre-trained accuracy of 95.4%. \" class=\"wp-image-1041579\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Sibyl_blog_figure4.jpg 1400w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Sibyl_blog_figure4-300x167.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Sibyl_blog_figure4-1024x569.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Sibyl_blog_figure4-768x427.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/Sibyl_blog_figure4-240x133.jpg 240w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><figcaption class=\"wp-element-caption\">Figure 4. Fine-tuning results on telemetry workload changes.<\/figcaption><\/figure>\n\n\n\n<p>To address slow dashboard performance, we tested SIBYL by using it to create materialized views\u2014special data structures that make queries run faster. These views identify common tasks and recommend which ones to store in advance, expediting future queries.<\/p>\n\n\n\n<p>We trained SIBYL using 2,237 queries from anonymized Microsoft sales data over 20 days, enabling us to create materialized views for the following day. Using historical data improved query performance 1.06 times, while SIBYL\u2019s predictions achieved a 1.83-time increase. This demonstrates that SIBYL\u2019s ability to forecast future workloads can significantly improve database performance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"implications-and-looking-ahead\">Implications and looking ahead<\/h3>\n\n\n\n<p>SIBYL\u2019s ability to predict dynamic workloads has numerous applications beyond improving materialized views. It can help organizations efficiently scale resources, leading to reduced costs. It can also improve query performance by automatically organizing data, ensuring that the most frequently accessed data is always available. Moving forward, we plan to integrate more machine learning techniques, making SIBYL even more efficient, reducing the effort needed for setup, and improving how databases handle dynamic workloads, making them faster and more reliable.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"acknowledgments\">Acknowledgments<\/h2>\n\n\n\n<p>We would like to thank our paper co-authors for their valuable contributions and efforts: Jyoti Leeka, Alekh Jindal, and Jishen Zhao.<\/p>\n<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span>","protected":false},"excerpt":{"rendered":"<p>SIBYL is a machine learning model that makes highly accurate predictions of database queries, enabling tuning for more efficiency. Applying traditional database optimizations to these predicted queries helps maintain high performance as demands change.<\/p>\n","protected":false},"author":37583,"featured_media":1041537,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"categories":[1],"tags":[],"research-area":[13563],"msr-region":[],"msr-event-type":[],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[684024],"related-projects":[],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Rana Alotaibi","user_id":42168,"display_name":"Rana Alotaibi","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/ranaalotaibi\/\" aria-label=\"Visit the profile page for Rana Alotaibi\">Rana Alotaibi<\/a>","is_active":false,"last_first":"Alotaibi, Rana","people_section":0,"alias":"ranaalotaibi"},{"type":"guest","value":"hanxian-huang","user_id":"1041612","display_name":"Hanxian Huang","author_link":"<a href=\"https:\/\/www.linkedin.com\/in\/hanxian-huang-729503169\/\" aria-label=\"Visit the profile page for Hanxian Huang\">Hanxian Huang<\/a>","is_active":true,"last_first":"Huang, Hanxian","people_section":0,"alias":"hanxian-huang"},{"type":"user_nicename","value":"Tarique Siddiqui","user_id":39645,"display_name":"Tarique Siddiqui","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/tasidd\/\" aria-label=\"Visit the profile page for Tarique Siddiqui\">Tarique Siddiqui<\/a>","is_active":false,"last_first":"Siddiqui, Tarique","people_section":0,"alias":"tasidd"},{"type":"user_nicename","value":"Carlo Curino","user_id":31352,"display_name":"Carlo Curino","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/ccurino\/\" aria-label=\"Visit the profile page for Carlo Curino\">Carlo Curino<\/a>","is_active":false,"last_first":"Curino, Carlo","people_section":0,"alias":"ccurino"},{"type":"user_nicename","value":"Jes\u00fas Camacho Rodr\u00edguez","user_id":40693,"display_name":"Jes\u00fas Camacho Rodr\u00edguez","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jesusca\/\" aria-label=\"Visit the profile page for Jes\u00fas Camacho Rodr\u00edguez\">Jes\u00fas Camacho Rodr\u00edguez<\/a>","is_active":false,"last_first":"Camacho Rodr\u00edguez, Jes\u00fas","people_section":0,"alias":"jesusca"},{"type":"user_nicename","value":"Yuanyuan Tian","user_id":40708,"display_name":"Yuanyuan Tian","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/yuanyuantian\/\" aria-label=\"Visit the profile page for Yuanyuan Tian\">Yuanyuan Tian<\/a>","is_active":false,"last_first":"Tian, Yuanyuan","people_section":0,"alias":"yuanyuantian"}],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-960x540.png\" class=\"img-object-cover\" alt=\"SIGMOD\/PODS 2024 logo to the left of the first page of accepted paper, &quot;SIBYL: Forecasting Time-Evolving Query Workloads&quot;\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-960x540.png 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-300x169.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-1024x576.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-768x432.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-1066x600.png 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-655x368.png 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-240x135.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-640x360.png 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1-1280x720.png 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prodnew\/2024\/05\/SIGMOD-Sibyl-2024-BlogHeroFeature-1400x788-1.png 1400w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/>","byline":"","formattedDate":"June 11, 2024","formattedExcerpt":"SIBYL is a machine learning model that makes highly accurate predictions of database queries, enabling tuning for more efficiency. 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