{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mlos-an-infrastructure-for-automatedsoftware","title":"MLOS: An Infrastructure for Automated Software Performance Engineering","arxiv_id":"2006.02155","date":"2020-06-01","proceeding":null,"authors":["Carlo Curino","Neha Godwal","Brian Kroth","Sergiy Kuryata","Greg Lapinski","Si-Qi Liu","Slava Oks","Olga Poppe","Adam Smiechowski","Ed Thayer","Markus Weimer","Yiwen Zhu"],"abstract":"Developing modern systems software is a complex task that combines business logic programming and Software Performance Engineering (SPE). The later is an experimental and labor-intensive activity focused on optimizing the system for a given hardware, software, and workload (hw/sw/wl) context. Today's SPE is performed during build/release phases by specialized teams, and cursed by: 1) lack of standardized and automated tools, 2) significant repeated work as hw/sw/wl context changes, 3) fragility induced by a \"one-size-fit-all\" tuning (where improvements on one workload or component may impact others). The net result: despite costly investments, system software is often outside its optimal operating point - anecdotally leaving 30% to 40% of performance on the table. The recent developments in Data Science (DS) hints at an opportunity: combining DS tooling and methodologies with a new developer experience to transform the practice of SPE. In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering. MLOS enables continuous, instance-level, robust, and trackable systems optimization. MLOS is being developed and employed within Microsoft to optimize SQL Server performance. Early results indicated that component-level optimizations can lead to 20%-90% improvements when custom-tuning for a specific hw/sw/wl, hinting at a significant opportunity. However, several research challenges remain that will require community involvement. To this end, we are in the process of open-sourcing the MLOS core infrastructure, and we are engaging with academic institutions to create an educational program around Software 2.0 and MLOS ideas.","url_abs":"https://arxiv.org/abs/2006.02155v2","url_pdf":"https://arxiv.org/pdf/2006.02155v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mlos-an-infrastructure-for-automatedsoftware","repo_url":"https://github.com/microsoft/MLOS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}