MTS-CR: Contrastive Representation Learning for Real-Time Qos Degradation Detection in Media Cloud Shared Instances

In media cloud environments, shared instances often suffer from unpredictable QoS degradation, leading to impaired user experience in real-time multimedia services such as HD video streaming. Existing methods struggle to model the high-dimensional, nonlinear telemetry dynamics that precede performance drops. To address this, we propose MTS-CR, a novel framework that combines contrastive learning and representation learning for real-time QoS degradation detection. MTS-CR employs a Linear Attention-based Encoder enhanced by Contrastive Predictive Coding (CPC) to extract discriminative temporal representations from 73 system-level metrics (CPU, memory, I/O, network). A lightweight MLP-based Predictor then forecasts imminent QoS anomalies. Evaluated on real-world cloud telemetry, MTS-CR significantly outperforms state-of-the-art baselines in both detection accuracy and generalization, offering cloud operators an actionable signal for dynamic resource orchestration — ultimately preserving end-user QoE.