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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11320 |
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| _version_ | 1866909537317945344 |
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| author | Qing, Yunfan Zheng, Wenli |
| author_facet | Qing, Yunfan Zheng, Wenli |
| contents | Dynamic scaling is critical to stream processing engines, as their long-running nature demands adaptive resource management. Existing scaling approaches easily cause performance degradation due to coarse-grained synchronization and inefficient state migration, resulting in system halt or high processing latency. In this paper, we propose DRRS, an on-the-fly scaling method that reduces performance overhead at the system level with three key innovations: (i) fine-grained scaling signals coupled with a re-routing mechanism that significantly mitigates propagation delay, (ii) a sophisticated record-scheduling mechanism that substantially reduces processing suspension, and (iii) subscale division, a mechanism that partitions migrating states into independent subsets, thereby reducing dependency-related overhead to enable finer-grained control and better runtime adaptability during scaling. DRRS is implemented on Apache Flink and, when compared to state-of-the-art approaches, reduces peak and average latencies by up to 81.1% and 95.5% respectively, while achieving a 72.8%-86% reduction in scaling duration, without disruption in non-scaling periods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11320 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Fine-Grained Scalability for Stateful Stream Processing Systems Qing, Yunfan Zheng, Wenli Distributed, Parallel, and Cluster Computing Dynamic scaling is critical to stream processing engines, as their long-running nature demands adaptive resource management. Existing scaling approaches easily cause performance degradation due to coarse-grained synchronization and inefficient state migration, resulting in system halt or high processing latency. In this paper, we propose DRRS, an on-the-fly scaling method that reduces performance overhead at the system level with three key innovations: (i) fine-grained scaling signals coupled with a re-routing mechanism that significantly mitigates propagation delay, (ii) a sophisticated record-scheduling mechanism that substantially reduces processing suspension, and (iii) subscale division, a mechanism that partitions migrating states into independent subsets, thereby reducing dependency-related overhead to enable finer-grained control and better runtime adaptability during scaling. DRRS is implemented on Apache Flink and, when compared to state-of-the-art approaches, reduces peak and average latencies by up to 81.1% and 95.5% respectively, while achieving a 72.8%-86% reduction in scaling duration, without disruption in non-scaling periods. |
| title | Towards Fine-Grained Scalability for Stateful Stream Processing Systems |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.11320 |