Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments

• URL: http://arxiv.org/abs/2608.18555v1
• Date: Wed, 19 Aug 2026 05:24:44 GMT
• Title: Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments
• Abstract summary: We propose a novel ML Performance Drift Detection framework for IoT environments.<n>The framework first employs an ML extraction model that learns service behavior from input-output pairs.<n>We further design an Adaptive Performance Drift Detection Mechanism that adjusts monitoring frequency.
• Score: 0.06157382820537719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT) environments, widely adopted across healthcare, smart homes, and industry due to its cost-effectiveness. However, the dynamic nature of IoT frequently alters data distributions, affecting MLaaS stability, while periodic MLaaS updates further introduce performance drift. Unlike traditional ML systems, MLaaS clients operate as black-box users without access to internal data or parameters, making drift detection particularly challenging. To address this, we propose a novel MLaaS Performance Drift Detection framework for IoT environments. The framework first employs an MLaaS extraction model that learns service behavior from input-output pairs and identifies prediction-influenced features. Building on this, the proposed MLaaS Performance Drift Detection (MPDD) model jointly captures variations in input data and MLaaS behavior. We further design an Adaptive-Temporal Performance Drift Detection Mechanism (APDDM) that dynamically adjusts monitoring frequency based on behavioral and data variations, enabling timely drift detection for effective service management. Extensive experiments on real-world datasets demonstrate that MPDD achieves up to 22-25% accuracy improvement over baseline drift detection methods. APDDM provides an average accuracy gain of approximately 4% and reduces the miss detection rate by around 9% compared to fixed-interval monitoring.

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