KnowTune: An Intelligent Configuration Tuning Framework Integrating Large Language Models and Bayesian Optimization

The KnowTune framework, open-sourced by the research team from Beihang University, addresses issues such as low efficiency of traditional manual tuning and slow cold start of Bayesian optimization by combining semantic system state modeling, LLM knowledge reasoning, and weighted prior-guided Bayesian optimization. It achieves high-performance automatic system configuration tuning across application scenarios. The framework supports human-machine collaboration, significantly improves sample efficiency, and has been open-sourced for community use.

The configuration space of modern software systems grows exponentially. Manual tuning relies on experience and is inefficient; in automatic tuning, Bayesian optimization has high sample efficiency but lacks the utilization of semantic and prior knowledge, leading to slow cold-start convergence. KnowTune is proposed in this context to integrate knowledge reasoning and optimization algorithms to solve these problems.

KnowTune adopts a three-layer architecture: 1. Semantic system state modeling: Convert system features into structured semantic representations to lay the foundation for knowledge transfer; 2. LLM-driven knowledge reasoning: Extract insights from documents and historical experiences to generate weighted prior suggestions; 3. Weighted prior-guided Bayesian optimization: Incorporate LLM suggestions as priors into the Gaussian process surrogate model, dynamically adjust weights to balance knowledge and data-driven approaches.

1. Cross-scenario generalization: Unify different system configuration problems through a semantic abstraction layer, enabling knowledge transfer; 2. Human-machine collaboration: Convert experts' natural language experiences into optimization priors, lowering the coding threshold; 3. Sample efficiency: Experiments show a 40%-60% reduction in evaluation rounds compared to traditional Bayesian optimization, reducing costs.

Applicable to scenarios such as database systems (optimizing parameters based on workloads), distributed computing frameworks (resource scheduling and execution configuration for Spark/Flink), machine learning training (optimizing batch size, learning rate, etc.), improving performance and efficiency.

KnowTune has been open-sourced on GitHub, with the core implemented in Python and compatible with mainstream toolchains; it provides example code and benchmark datasets to lower the entry barrier and supports integration into existing operation and maintenance processes.

Facing challenges such as knowledge representation standardization and online continuous optimization; in the future, we can explore integrating multi-modal large models with system monitoring, logs, and other information to further enhance the level of intelligence.

KnowTune integrates LLM knowledge reasoning and classic optimization algorithms to provide an intelligent, efficient, and generalizable solution for system tuning, which has important reference value for operation and maintenance teams and technical decision-makers.