生成可配置的MLaaS数据集，助力物联网环境下的模型服务评估
Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environments
- 通过模拟真实场景训练多种模型，生成可复现的服务数据
- 构建超万级服务实例，提升选择与组合的准确率和质量
- 适合研究MLaaS选型与集成的开发者与学者使用
我们提出一种新型的MLaaS数据集生成框架（MDG），用于生成可配置、可复现的数据集，以评估机器学习即服务（MLaaS）的选择与组合。MDG通过在多个真实数据集和数据分布设置下训练与评估多样化模型族，模拟真实的MLaaS行为。它记录详细的函数属性、服务质量指标及组合特异性指标，支持对服务性能与跨服务行为的系统分析。利用MDG，我们生成了超过一万个MLaaS服务实例，并构建了一个大规模基准数据集，适用于下游评估。此外，框架内置组合机制，可建模服务在不同物联网条件下的交互。实验表明，相比现有基线，MDG生成的数据集显著提升了选择准确率与组合质量。该框架为推进数据驱动的MLaaS选型与组合研究提供了实用且可扩展的基础。
原文摘要 · Abstract (English)
We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and composition. MDG simulates realistic MLaaS behaviour by training and evaluating diverse model families across multiple real-world datasets and data distribution settings. It records detailed functional attributes, quality of service metrics, and composition-specific indicators, enabling systematic analysis of service performance and cross-service behaviour. Using MDG, we generate more than ten thousand MLaaS service instances and construct a large-scale benchmark dataset suitable for downstream evaluation. We also implement a built-in composition mechanism that models how services interact under varied Internet of Things conditions. Experiments demonstrate that datasets generated by MDG enhance selection accuracy and composition quality compared to existing baselines. MDG provides a practical and extensible foundation for advancing data-driven research on MLaaS selection and composition
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