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This paper introduces a novel framework for detecting performance drift in Machine Learning as a Service (MLaaS) specifically tailored for IoT environments, addressing the challenges posed by the dynamic nature of IoT data distributions. The framework includes an MLaaS extraction model that learns service behavior from input-output pairs and identifies key features influencing predictions, alongside an Adaptive-Temporal Performance Drift Detection Mechanism (APDDM) that optimizes monitoring frequency based on observed variations. Experimental results show that the proposed methods achieve significant accuracy improvements, with MPDD enhancing drift detection accuracy by 22-25% and APDDM reducing the miss detection rate by approximately 9% compared to traditional approaches.
MLaaS performance drift can be detected with up to 25% greater accuracy using a novel framework that adapts to IoT data dynamics.
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.
