Recent stream computing systems have shifted from static, threshold-based straggler detection to adaptive, temporal-pattern-based prediction. Existing prediction approaches typically rely on autoregressive time-series analysis to model each node’s historical resource usage within a cluster. However, they often overlook cross-node state correlations, resulting in isolated prediction models that fail to capture the dynamic spatio-temporal dependencies among nodes. To address these limitations, we propose Ps-Stream, a proactive framework for straggler prediction and mitigation. First, Ps-Stream constructs a unified spatio-temporal graph by modeling compute nodes as a spatial graph at each time step and linking each node across adjacent time steps. Second, it learns spatio-temporal representations for straggler prediction by combining Graph Convolutional Networks (GCNs) to capture spatial dependencies with attention mechanisms to model temporal dynamics. Third, it incorporates a fine-grained task migration strategy that proactively reallocates tasks from predicted stragglers to the least-loaded nodes while considering task dependencies and migration costs to minimize migration overhead. Experimental results show that Ps-Stream improves straggler prediction accuracy by up to 26%, reduces maximum system latency by 62%, more than doubles maximum throughput, and improves resource utilization by up to 34% compared with state-of-the-art baselines.
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