{"id":1141566,"date":"2025-06-06T14:43:52","date_gmt":"2025-06-06T21:43:52","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=1141566"},"modified":"2025-07-08T10:34:32","modified_gmt":"2025-07-08T17:34:32","slug":"autotuning-systems-techniques-challenges-and-opportunities","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/autotuning-systems-techniques-challenges-and-opportunities\/","title":{"rendered":"Autotuning Systems: Techniques, Challenges, and Opportunities"},"content":{"rendered":"<p class=\"p1\">The rapid growth of cloud computing and systems has introduced <span class=\"s1\">significant complexity in managing and optimizing configurations <\/span><span class=\"s1\">to meet diverse workload demands across a wide array of hardware. <\/span><span class=\"s1\">Autotuning systems, leveraging advancements in machine learning <\/span>and optimization, offer an effective solution to these challenges. By automating configuration tuning, these systems can dynamically adapt to workload changes, optimize performance in real <span class=\"s1\">time, and reduce the burden on system administrators. This tutorial <\/span><span class=\"s1\">provides both theoretical foundations and practical demonstrations <\/span><span class=\"s1\">of systems autotuning software, with a focus on offline and online <\/span>optimization methodologies. We present a comprehensive review of state-of-the-art autotuning systems and discuss how they ad<span class=\"s2\">dress key challenges, such as handling large configuration spaces, <\/span><span class=\"s1\">mitigating noise in real-world environments, and ensuring safe and <\/span>efficient exploration during tuning. To conclude, we offer a hands-<span class=\"s1\">on session where participants can experiment with an open-source <\/span><span class=\"s2\">system and gain experience with real-world tuning scenarios.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The rapid growth of cloud computing and systems has introduced significant complexity in managing and optimizing configurations to meet diverse workload demands across a wide array of hardware. Autotuning systems, leveraging advancements in machine learning and optimization, offer an effective solution to these challenges. By automating configuration tuning, these systems can dynamically adapt to workload [&hellip;]<\/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":"","msr_journal":"","msr_number":"","msr_organization":"ACM","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"SIGMOD 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