mlsanityData to 14 Oct 2026

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

arXiv 2602.06555 ↗First posted 6 Feb 2026cs.DC cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 26 references that are in the map. First posted on arXiv 6 Feb 2026.

Builds on

3 of 26 references.

References · 3

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2023A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functionscited by 5
  3. 2023FMI: Fast and Cheap Message Passing for Serverless Functionscited by 3

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