Reinforcement Learning-Based Dynamic Management of Structured Parallel Farm Skeletons on Serverless Platforms

TL;DR

This paper introduces a reinforcement learning framework for dynamic management of structured parallel skeletons on serverless platforms, aiming to improve performance and resilience while maintaining programmability.

Contribution

It presents a novel RL-based autoscaling approach for farm patterns on serverless platforms, combining monitoring, control, and learning for optimized resource management.

Findings

RL policies outperform reactive management in QoS metrics

AI-driven scaling adapts better to platform limitations

Improved resource efficiency and stable scaling behavior

Abstract

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.