用大模型生成调度策略，模拟器验证并迭代优化云服务性能。
AI for Distributed Systems Design: Scalable Cloud Optimization Through Repeated LLMs Sampling And Simulators
- 大模型生成调度代码，模拟器在真实负载下评估反馈。
- 多模型测试中吞吐量提升显著，验证了方法有效性。
- 适合研究云系统优化与AI辅助设计的开发者参考。
我们通过将大型语言模型（LLMs）的随机代码生成与领域专用模拟器中的确定性验证相结合，探索了AI驱动的分布式系统策略设计。以函数即服务运行时（Bauplan）及其开源模拟器（Eudoxia）为例，将调度器设计建模为迭代的生成-验证循环：LLM提出Python策略，模拟器在标准负载轨迹上评估其表现，并通过结构化反馈指导后续生成。该框架在保持可解释性的同时，实现了对大规模设计空间的定向搜索。本文详细描述了系统架构，并报告了多个模型上的吞吐量提升结果。尽管初期效果显著，但我们也讨论了当前设置的局限性，并指出未来关键方向：借助AI自动生成新的模拟器以实现方法规模化。
原文摘要 · Abstract (English)
We explore AI-driven distributed-systems policy design by combining stochastic code generation from large language models (LLMs) with deterministic verification in a domain-specific simulator. Using a Function-as-a-Service runtime (Bauplan) and its open-source simulator (Eudoxia) as a case study, we frame scheduler design as an iterative generate-and-verify loop: an LLM proposes a Python policy, the simulator evaluates it on standardized traces, and structured feedback steers subsequent generations. This setup preserves interpretability while enabling targeted search over a large design space. We detail the system architecture and report preliminary results on throughput improvements across multiple models. Beyond early gains, we discuss the limits of the current setup and outline next steps; in particular, we conjecture that AI will be crucial for scaling this methodology by helping to bootstrap new simulators.
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