StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems

• URL: http://arxiv.org/abs/2510.25017v1
• Date: Tue, 28 Oct 2025 22:33:14 GMT
• Title: StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems
• Authors: Qi Lin, Zhenyu Zhang, Viraj Thakkar, Zhenjie Sun, Mai Zheng, Zhichao Cao,
• Abstract summary: Heuristic and ML tuning are often system specific, require manual glue, and degrade under changes.<n>Recent LLM-based approaches help but usually treat tuning as a single-shot, system-specific task.<n>We present StorageXTuner, an LLM agent-driven auto-tuning framework for heterogeneous storage engines.<n>We implement a prototype and evaluate it on RocksDB, LevelDB, CacheLib, and InnoDB with YCSB, MixGraph, and TPC-H/C.
• Score: 9.148071923560414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatically configuring storage systems is hard: parameter spaces are large and conditions vary across workloads, deployments, and versions. Heuristic and ML tuners are often system specific, require manual glue, and degrade under changes. Recent LLM-based approaches help but usually treat tuning as a single-shot, system-specific task, which limits cross-system reuse, constrains exploration, and weakens validation. We present StorageXTuner, an LLM agent-driven auto-tuning framework for heterogeneous storage engines. StorageXTuner separates concerns across four agents - Executor (sandboxed benchmarking), Extractor (performance digest), Searcher (insight-guided configuration exploration), and Reflector (insight generation and management). The design couples an insight-driven tree search with layered memory that promotes empirically validated insights and employs lightweight checkers to guard against unsafe actions. We implement a prototype and evaluate it on RocksDB, LevelDB, CacheLib, and MySQL InnoDB with YCSB, MixGraph, and TPC-H/C. Relative to out-of-the-box settings and to ELMo-Tune, StorageXTuner reaches up to 575% and 111% higher throughput, reduces p99 latency by as much as 88% and 56%, and converges with fewer trials.

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