Learning Unified System Representations for Microservice Tail Latency Prediction

• URL: http://arxiv.org/abs/2508.01635v1
• Date: Sun, 03 Aug 2025 07:46:23 GMT
• Title: Learning Unified System Representations for Microservice Tail Latency Prediction
• Authors: Wenzhuo Qian, Hailiang Zhao, Tianlv Chen, Jiayi Chen, Ziqi Wang, Kingsum Chow, Shuiguang Deng,
• Abstract summary: Microservice architectures have become the de facto standard for building scalable cloud-native applications.<n>Traditional approaches often rely on per-request latency metrics, which are highly sensitive to transient noise.<n>We propose USRFNet, a deep learning network that explicitly separates and models traffic-side and resource-side features.
• Score: 8.532290784939967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microservice architectures have become the de facto standard for building scalable cloud-native applications, yet their distributed nature introduces significant challenges in performance monitoring and resource management. Traditional approaches often rely on per-request latency metrics, which are highly sensitive to transient noise and fail to reflect the holistic behavior of complex, concurrent workloads. In contrast, window-level P95 tail latency provides a stable and meaningful signal that captures both system-wide trends and user-perceived performance degradation. We identify two key shortcomings in existing methods: (i) inadequate handling of heterogeneous data, where traffic-side features propagate across service dependencies and resource-side signals reflect localized bottlenecks, and (ii) the lack of principled architectural designs that effectively distinguish and integrate these complementary modalities. To address these challenges, we propose USRFNet, a deep learning network that explicitly separates and models traffic-side and resource-side features. USRFNet employs GNNs to capture service interactions and request propagation patterns, while gMLP modules independently model cluster resource dynamics. These representations are then fused into a unified system embedding to predict window-level P95 latency with high accuracy. We evaluate USRFNet on real-world microservice benchmarks under large-scale stress testing conditions, demonstrating substantial improvements in prediction accuracy over state-of-the-art baselines.

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