Bayesian Optimization
Learn how our Bayesian optimization engine automates OS tuning to accelerate launch time and reduce manual experimentation.
CHALLENGE
Optimizing operating‑system parameters used to be an art form. Engineers manually tweaked settings like ZRAM size and “swappiness” and hoped for the best. Multiple OS parameters influence application launch time, creating a multidimensional search space that was impossible to explore manually.
SOLUTION
We pioneered an automated tuning process based on Bayesian optimisation. The tool models the relationship between configuration settings and launch time, proposes experiments and iteratively converges on the optimal configuration.
Impact
The data‑driven approach cut staff hours previously spent on manual tuning and delivered measurable performance gains. The tool’s success led to its adoption on several other projects, demonstrating how machine learning can streamline systems engineering.
