动态调整物联网中的机器学习服务组合，提升稳定性和效率
Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments
- 用上下文多臂赌博机策略逐步优化服务组合
- 在真实数据集上降低重构计算成本，保持服务质量
- 适合资源受限的物联网场景下的智能服务管理
物联网环境的动态特性挑战了机器学习即服务（MLaaS）组合的长期有效性。环境不确定性与变化导致数据分布波动，如概念漂移和数据异构性，以及系统需求演变，如可扩展性要求和资源限制。本文提出一种自适应MLaaS组合框架，确保服务组合的无缝、高效与可扩展。该框架集成服务评估模型以识别性能不佳的MLaaS服务，并通过候选选择模型筛选最优替代方案。开发了一种自适应组合机制，采用上下文多臂赌博机优化策略增量更新组合。通过持续适应不断变化的物联网约束，在维持服务质量的同时，显著降低从头重构的计算开销。在真实世界数据集上的实验结果验证了该方法的高效性。
原文摘要 · Abstract (English)
The dynamic nature of Internet of Things (IoT) environments challenges the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. The uncertainty and variability of IoT environments lead to fluctuations in data distribution, e.g., concept drift and data heterogeneity, and evolving system requirements, e.g., scalability demands and resource limitations. This paper proposes an adaptive MLaaS composition framework to ensure a seamless, efficient, and scalable MLaaS composition. The framework integrates a service assessment model to identify underperforming MLaaS services and a candidate selection model to filter optimal replacements. An adaptive composition mechanism is developed that incrementally updates MLaaS compositions using a contextual multi-armed bandit optimization strategy. By continuously adapting to evolving IoT constraints, the approach maintains Quality of Service (QoS) while reducing the computational cost associated with recomposition from scratch. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.
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