Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environments

TL;DR

This paper introduces MDG, a framework that generates large, configurable datasets simulating MLaaS environments for IoT, aiding in better service selection and composition evaluation.

Contribution

The paper presents a novel, extensible framework for generating realistic MLaaS datasets, including a composition mechanism for IoT scenarios, improving evaluation accuracy.

Findings

Generated over ten thousand MLaaS service instances

Datasets improve selection accuracy over baselines

Enhances evaluation of MLaaS in IoT environments

Abstract

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