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MGroup: Multi-Instance Workload Prediction Approach Based on Group Behavior Perception
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AI
DOI：10.1109/TPDS.2026.3677032.png)
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Abstract
En 中文
Workload prediction is a key step in artificial intelligence for IT operations (AIOps) on cloud platforms, enabling proactive application management for performance assurance, cost reduction, and energy optimization. With the popularity of microservice architectures, user requests are now handled collaboratively by multiple service instances, so the workload variation is no longer the individual behavior of each instance but the group behavior of multiple instances. However, existing approaches typically analyze each instance independently and fail to explicitly model group-level workload evolution, leading to suboptimal predictions. To address this issue, we propose MGroup, a workload group behavior-aware multi-instance workload prediction method. First, we define the concept of highly-coupled multi-instance workload group behavior and its evolution, shifting the analytical focus from individuals to groups; second, we propose an adaptive method for identifying and characterizing the workload group behavior based on both static and dynamic correlations, shifting from similarity-based to correlation-based representation; third, we propose a multi-instance parallel prediction neural network that jointly captures local and global workload evolution, shifting from implicit modeling to explicit modeling. Based on this approach, we design a workload prediction system tailored to cloud-native applications. Finally, experimental results on public datasets show that MGroup reduces MAE by 14.62%-21.60% and RMSE by 21.97%-29.27% compared to existing state-of-the-art methods, which provides an effective solution for realizing workload prediction for cloud-native applications.
Keywords:
Cloud computing
microservices
workload
time series prediction
AIOps
Journal
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6
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5.2K
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