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

2026-02-06

AI Summary

This work addresses the challenges of performance, reliability, and quality-of-service (QoS) guarantees in structured parallel computing on serverless platforms by proposing an intelligent auto-scaling approach that integrates reinforcement learning with the Farm parallel skeleton. A monitoring and control layer built on OpenFaaS leverages the Gymnasium framework to dynamically adjust the number of worker nodes based on real-time queue states, temporal features, and QoS metrics. To the best of our knowledge, this is the first effort to apply reinforcement learning to the serverless deployment of structured parallel skeletons. The proposed method significantly improves resource utilization while satisfying QoS constraints, outperforming conventional model-driven and reactive baselines in achieving stable and efficient auto-scaling.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• serverless
• structured parallel skeletons
• autoscaling
• QoS-aware resource management
• reinforcement learning

Innovation

• Reinforcement Learning
• Serverless Computing
• Structured Parallel Skeletons
• Autoscaling
• QoS-aware Resource Management