MLOS: An Infrastructure for Automated Software Performance Engineering
| Autor: | Curino, Carlo, Godwal, Neha, Kroth, Brian, Kuryata, Sergiy, Lapinski, Greg, Liu, Siqi, Oks, Slava, Poppe, Olga, Smiechowski, Adam, Thayer, Ed, Weimer, Markus, Zhu, Yiwen |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3399579.3399927 |
| Popis: | 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. Comment: 4 pages, DEEM 2020 |
| Databáze: | arXiv |
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