  Microsoft's internal big-data infrastructure is one of the largest in the
world -- with over 300k machines running billions of tasks from over 0.6M daily
jobs. Operating this infrastructure is a costly and complex endeavor, and
efficiency is paramount. In fact, for over 15 years, a dedicated engineering
team has tuned almost every aspect of this infrastructure, achieving
state-of-the-art efficiency (>60% average CPU utilization across all clusters).
Despite rich telemetry and strong expertise, faced with evolving
hardware/software/workloads this manual tuning approach had reached its limit
-- we had plateaued.
  In this paper, we present KEA, a multi-year effort to automate our tuning
processes to be fully data/model-driven. KEA leverages a mix of domain
knowledge and principled data science to capture the essence of our cluster
dynamic behavior in a set of machine learning (ML) models based on collected
system data. These models power automated optimization procedures for parameter
tuning, and inform our leadership in critical decisions around engineering and
capacity management (such as hardware and data center design, software
investments, etc.). We combine "observational" tuning (i.e., using models to
predict system behavior without direct experimentation) with judicious use of
"flighting" (i.e., conservative testing in production). This allows us to
support a broad range of applications that we discuss in this paper.
  KEA continuously tunes our cluster configurations and is on track to save
Microsoft tens of millions of dollars per year. At the best of our knowledge,
this paper is the first to discuss research challenges and practical learnings
that emerge when tuning an exabyte-scale data infrastructure.
