BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters

TL;DR

BoGraph introduces a structured Bayesian optimization framework that learns system dependencies from logs, enabling faster and more efficient tuning of complex, high-dimensional computer systems.

Contribution

It presents BoAnon, a novel SBO framework that learns system structure from logs and incorporates expert knowledge into probabilistic graph models for improved optimization.

Findings

Achieved 5-7x improvement in energy-latency trade-offs.

Enabled faster convergence in system configuration tuning.

Demonstrated applicability to hardware, databases, and stream processors.

Abstract

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.