BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters
This work addresses the challenge of auto-tuning complex computer systems like databases and stream processors, making optimization more accessible, though it is incremental by building on existing SBO methods.
The paper tackled the problem of tuning expensive computer systems with many parameters by proposing BoAnon, a Structured Bayesian Optimization framework that learns system structure from logs, resulting in a 5-7x improvement in energy-latency objectives over default architectures in hardware architecture search.
Current auto-tuning frameworks struggle with tuning computer systems configurations due to their large parameter space, complex interdependencies, and high evaluation cost. Utilizing probabilistic models, Structured Bayesian Optimization (SBO) has recently overcome these difficulties. SBO decomposes the parameter space by utilizing contextual information provided by system experts leading to fast convergence. However, the complexity of building probabilistic models has hindered its wider adoption. We propose BoAnon, a SBO framework that learns the system structure from its logs. BoAnon provides an API enabling experts to encode knowledge of the system as performance models or components dependency. BoAnon takes in the learned structure and transforms it into a probabilistic graph model. Then it applies the expert-provided knowledge to the graph to further contextualize the system behavior. BoAnon probabilistic graph allows the optimizer to find efficient configurations faster than other methods. We evaluate BoAnon via a hardware architecture search problem, achieving an improvement in energy-latency objectives ranging from $5-7$ x-factors improvement over the default architecture. With its novel contextual structure learning pipeline, BoAnon makes using SBO accessible for a wide range of other computer systems such as databases and stream processors.