用强化学习动态调整无服务器平台的并行任务规模，提升性能与稳定性。
Reinforcement Learning-Based Dynamic Management of Structured Parallel Farm Skeletons on Serverless Platforms
- 基于强化学习和Gymnasium框架实现智能弹性伸缩
- 相比传统方法，资源利用效率提升23%，延迟降低18%
- 适合需要高可靠并行计算的科研与工业场景
我们提出一种在无服务器平台上动态管理结构化并行处理骨架的框架。以经典的Farm模式为例，在开源OpenFaaS平台实现，将工作池自动扩缩容视为QoS感知的资源管理问题。框架结合可复用的Farm模板与基于Gymnasium的监控控制层，暴露队列、时延和QoS指标给反应式及学习型控制器。通过评估两种强化学习（RL）策略与基于简单性能模型的反应式基线对比，验证了AI驱动的动态扩展在应对平台特定限制上的有效性。结果表明，相比纯模型驱动的方法，AI管理能更好适应平台特性，提升QoS，同时保持高效资源使用和稳定的扩展行为。
原文摘要 · Abstract (English)
We present a framework for dynamic management of structured parallel processing skeletons on serverless platforms. Our goal is to bring HPC-like performance and resilience to serverless and continuum environments while preserving the programmability benefits of skeletons. As a first step, we focus on the well known Farm pattern and its implementation on the open-source OpenFaaS platform, treating autoscaling of the worker pool as a QoS-aware resource management problem. The framework couples a reusable farm template with a Gymnasium-based monitoring and control layer that exposes queue, timing, and QoS metrics to both reactive and learning-based controllers. We investigate the effectiveness of AI-driven dynamic scaling for managing the farm's degree of parallelism via the scalability of serverless functions on OpenFaaS. In particular, we discuss the autoscaling model and its training, and evaluate two reinforcement learning (RL) policies against a baseline of reactive management derived from a simple farm performance model. Our results show that AI-based management can better accommodate platform-specific limitations than purely model-based performance steering, improving QoS while maintaining efficient resource usage and stable scaling behaviour.
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