PFTuner: An Efficient and Effective Multi-Objective Configuration Tuning Framework Adaptive to Different Software Systems

Software systems often expose a large number of configurable parameters to satisfy diverse application requirements and deployment scenarios. Given the intricate dependencies between parameters, manually finding a well-performing configuration is a daunting task even for experienced operators. Most existing automatic tuning approaches treat the problem as a single-objective search, leaving critical concerns such as energy consumption and reliability as afterthoughts. Although recent studies have explored multi-objective configuration tuning, they still face several challenges, including handling conflicting objectives, balancing search effectiveness and efficiency, and adapting to heterogeneous configuration spaces across different software systems. To address these issues, we propose PFTuner, an efficient and effective multi-objective configuration tuning framework adaptive to diverse software systems. PFTuner consists of three collaborative modules, namely Configuration Generator, Configuration Evaluator, and Sample Collector, which operate iteratively to continuously improve configuration quality. In particular, we design a novel multi-objective optimization algorithm that effectively models heterogeneous configuration spaces and improves the balance between optimization quality and search efficiency. We evaluate PFTuner on eight software-workload scenarios deployed on a local cluster and compare it with several representative state-of-the-art baselines. Experimental results show that PFTuner consistently achieves higher-quality Pareto fronts and better search efficiency, while also demonstrating strong adaptability across different software systems and workloads.