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arXiv · 2606.30848

StreamGuard: Low-Overhead Resilience for Real-time HPC Data Streams

Hai Duc Nguyen·Bogdan Nicolae·Tekin Bicer·Amal Gueroudji·Matthieu Dorier·Kyle Chard·Ian Foster
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Abstract

Real-time scientific workflows operate on continuous data streams and must produce timely, high-quality results despite executing on complex, failure-prone infrastructure. Hardware faults, network disruptions, and performance anomalies caused by resource contention or system heterogeneity can severely degrade performance and violate real-time constraints. We focus on strengthening the resilience of the producer-consumer streaming pattern, a fundamental building block of scientific streaming workflows. We present two complementary techniques: (i) a dynamic, asynchronous, non-blocking checkpointing mechanism that preserves progress without interrupting computation, and (ii) a progress-aware load redistribution strategy that detects slow workers and proactively rebalances tasks. Together, these mechanisms maintain forward progress and balanced execution even in highly error-prone environments. Experimental results show that our approach reduces the impact of failures and performance anomalies by up to 6x, while introducing less than 1% overhead in failure-free execution.

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Hai Duc Nguyen, Bogdan Nicolae, Tekin Bicer, Amal Gueroudji, Matthieu Dorier, Kyle Chard, Ian Foster. 2026-06-29. StreamGuard: Low-Overhead Resilience for Real-time HPC Data Streams. https://doi.org/10.1145/3797905.3807872

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