.png){kind=icon}
Return
Ps-Stream: Proactive Straggler Mitigation Using Spatio-Temporal Features for Elastic Stream Computing Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/tc.2026.3731957.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Stream computing systems
straggler prediction
straggler mitigation
graph convolutional network
attention mechanism
Journal
IF:
3.8
Papers:
5.4K
Citations:
9.8K
Organization
View more organizations
Cited Papers
No cited papers available