arXiv：cs.AI· Javier Mateos-Bravo, Sergio Laso, Juan Luis Herrera, Ilir Murturi, Pantelis Frangoudis, Schahram Dustdar·· 4 小时前AI 评分32
ARGOS：强化学习驱动的计算连续体服务编排多维弹性
ARGOS: Reinforcement Learning-Driven Multidimensional Elasticity for Service Orchestration in the Computing Continuum
AI 导读
ARGOS 是面向计算连续体的端到端控制器，将多维弹性建模为针对分析质量与集群压力的逐请求马尔可夫决策过程，并配合容量感知准入控制。在异构集群的受控负载与多租户时变到达测试中，其深度强化学习策略持续优于非学习基线，并接近独立调优的最佳固定参考。实际在线评估显示其相较静态中点方案有所改善，未记录 CPU 或内存违规，但仍存在覆盖率违规。
正文
Abstract: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.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.37085 [cs.DC] |
| (or arXiv:2609.37085v2 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2609.37085 arXiv-issued DOI via DataCite |
Submission history
From: Ilir Murturi Dr [view email]
[v1]
Tue, 29 Sep 2026 09:14:36 UTC (11,916 KB)
[v2]
Fri, 2 Oct 2026 13:44:47 UTC (11,943 KB)
来源：arXiv：cs.AI · arxiv.org