  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.
