CRAFTER：基于因果性的自主物联网系统自适应
CRAFTER: Causality-based Self-adaptation for Autonomous IoT Systems
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中文总结 AI 辅助
CRAFTER提出基于因果强化学习的自动化框架，通过生成因果图指导自适应决策，在动态物联网环境中将自适应性能提升25%。
中文摘要 AI 辅助
本文介绍了CRAFTER，一个使用因果强化学习（CRL）设计和部署自适应物联网系统的自动化框架。随着物联网设备日益普及于普适计算空间，智能环境得以实现先进的监控和交互服务。这些环境的动态特性，如工作负载波动和应用需求演变，对维持物联网应用一致的服务质量（QoS）水平构成了重大挑战。虽然现有的自适应技术提供了自适应能力，但它们往往针对特定应用领域设计，阻碍了可跨多个物联网垂直领域复用的自适应解决方案的设计。此外，缺乏自动化管道来识别关键性能驱动因素以做出有效的自适应决策。CRAFTER通过使用因果关系作为物联网系统性能分析的正式框架来解决这些问题。CRAFTER生成因果图以揭示系统组件间的依赖关系，并基于因果关系指导自适应决策。然后，自适应代理可以利用这些知识在动态情况下做出更有效的自适应决策。我们的实验评估展示了CRAFTER如何能够推导出覆盖多种物联网用例的因果图。此外，我们展示了CRAFTER相比最先进的基于强化学习的方法，将自适应性能提升了25%。
英文摘要
This paper presents CRAFTER, an automated framework for designing and deploying self-adaptive IoT systems using Causal Reinforcement Learning (CRL). As IoT devices increasingly populate pervasive computing spaces, smart environments are enabled with advanced monitoring and interactive services. The dynamic nature of these environments, such as fluctuating workloads and evolving application demands, poses significant challenges in maintaining consistent Quality of Service (QoS) levels of IoT applications. While existing self-adaptation techniques offer adaptive capabilities, they are often designed to deal with specific application domains, hindering the design of self-adaptive solutions that can be re-used across multiple IoT verticals. In addition, there is a lack of automated pipelines that act on identifying key performance drivers to take effective adaptation decisions. CRAFTER addresses these issues by using Causality as a formal framework for performance analysis of IoT systems. CRAFTER generates causal graphs to uncover dependencies among system components and guide adaptation decisions based on cause-effect relationships. Then, adaptation agents can leverage this knowledge to take more effective adaptation decisions in dynamic situations. Our experimental evaluation demonstrates how CRAFTER enables deriving causal graphs spanning diverse IoT use cases. Furthermore, we showcase how CRAFTER improves self-adaptation performance by 25% compared to state-of-the-art Reinforcement Learning-based approaches.
发表机构
- Ericsson Research Artificial Intelligence（爱立信人工智能研究院）
- University of Patras（帕特雷大学）
机构由 AI 辅助整理，请以论文原文为准。
