mlsanityData to 14 Oct 2026

ARGOS: Reinforcement Learning-Driven Multidimensional Elasticity for Service Orchestration in the Computing Continuum

arXiv 2609.37085 ↗First posted 29 Sep 2026cs.DC cs.AIcited by 0

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

Abstract (arXiv)

Data-intensive services in the Computing Continuum must balance analytics quality, resource usage, and cost across heterogeneous nodes with limited and uneven capacity. This balance becomes especially difficult when resource scaling reaches capacity limits, because changes in demand and cluster pressure must then be absorbed without violating client-defined quality ranges. Existing orchestrators mainly adapt resources, placements, or replicas, while analytics requirements such as coverage, sample, and freshness remain fixed. This article presents ARGOS, the Adaptive Reinforcement Learning-Driven Governance for Orchestrated Services, an end-to-end controller that formulates multidimensional elasticity as a per-request Markov decision process over analytics quality and cluster pressure, supported by capacity-aware admission. ARGOS is evaluated under controlled workloads and time-varying multi-tenant arrivals on a heterogeneous cluster. Across the controlled scenarios, the deep reinforcement learning policies consistently outperform the non-learning baselines and approach the independently tuned best-fixed reference. A separate live evaluation reports improvements over the static midpoint under realistic and saturated arrivals, with no recorded CPU or memory violations but remaining coverage violations. These results support deep reinforcement learning as an adaptive mechanism for multidimensional elasticity when resource scaling alone is insufficient.

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 9 of its 34 references that are in the map. First posted on arXiv 29 Sep 2026.

Builds on

9 of 34 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  3. 2017Deep Reinforcement Learning that Matterscited by 754
  4. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 322
  5. 2018Learning Scheduling Algorithms for Data Processing Clusterscited by 134
  6. 2018How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experimentscited by 60
  7. 2021A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environmentscited by 6
  8. 2023Deep Reinforcement Learning-based Scheduling for Optimizing System Load and Response Time in Edge and Fog Computing Environmentscited by 6
  9. 2025A Multidimensional Elasticity Framework for Adaptive Data Analytics Management in the Computing Continuumcited by 2

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