ARGOS：计算连续体中服务编排的强化学习驱动多维弹性

中文摘要 AI 辅助

计算连续体中的数据密集型服务必须在异构节点上平衡分析质量、资源使用和成本，同时这些节点的容量有限且不均衡。当资源扩展达到容量极限时，这种平衡变得尤为困难，因为需求变化和集群压力必须在不违反客户端定义的质量范围的情况下被吸收。现有编排器主要调整资源、放置或副本，而分析需求（如覆盖率、样本和新鲜度）保持不变。本文介绍了ARGOS，即自适应强化学习驱动的编排服务治理，一种端到端控制器，它将多维弹性形式化为基于分析质量和集群压力的每请求马尔可夫决策过程，并辅以容量感知的准入控制。ARGOS在受控工作负载和时变多租户到达的异构集群上进行了评估。在受控场景中，深度强化学习策略始终优于非学习基线，并接近独立调优的最佳固定参考。一项单独的实时评估报告了在现实且饱和的到达条件下相对于静态中点的改进，没有记录到CPU或内存违规，但仍存在覆盖率违规。这些结果支持深度强化学习作为资源扩展不足时多维弹性的自适应机制。

英文摘要

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.

发表机构

• University of Extremadura（埃斯特雷马杜拉大学）
• Global Process and Product Improvement S.L.（全球流程与产品改进有限公司）
• University of Prishtina（普里什蒂纳大学）
• TU Wien（维也纳工业大学）
• ICREA（加泰罗尼亚研究与高等教育机构）

机构由 AI 辅助整理，请以论文原文为准。