A cloud-native framework for seismic waveform data quality assessment: Performance evaluation under equivalent resource constraints
- 1The Second Monitoring and Application Center, China Earthquake Administration, Xi’an, China
Abstract
The rapid growth of seismic waveform data requires efficient and elastic processing, but virtual-machine-based architectures often tie workload execution to coarse resource units and rigid scaling policies. To address these limitations, we propose a Kubernetes- and Docker-based cloud-native framework for real-time seismic waveform data quality assessment. The framework organizes the processing logic into three tiers: infrastructure and orchestration, elastic scaling policy, and data quality assessment services. It further introduces an Adaptive Elastic Scheduling Algorithm (AESA) that combines CPU and memory high-watermark triggers with task-lifecycle-aware contraction for containerized computation of key quality indicators, including gap count, gap duration, and percent of availability. Under equivalent hardware resource constraints, the cloud-native implementation reduced mean response latency by 47.27% at 20 requests per second and 47.26% at 50 requests per second compared with the VM-based control group, corresponding to an approximately 1.90-fold speedup in both load regimes. These results indicate that, for the tested gap-count, gap-duration, and availability workloads, cloud-native orchestration can improve latency without changing the quality-metric code. The evaluation is limited to the specified workload, resource budget, and two traffic levels.
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BibTeX
@article{li2026cloudnative,
title = {{A cloud-native framework for seismic waveform data quality assessment: Performance evaluation under equivalent resource constraints}},
author = {Yang Li and Danning Wang and Runyuan Dong and Chen Cheng and Xiuping Zhang and Wenqing Wang and Chuanjin Liu},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357267},
url = {https://doi.org/10.1371/journal.pone.0357267},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A cloud-native framework for seismic waveform data quality assessment: Performance evaluation under equivalent resource constraints
AU - Yang Li
AU - Danning Wang
AU - Runyuan Dong
AU - Chen Cheng
AU - Xiuping Zhang
AU - Wenqing Wang
AU - Chuanjin Liu
PY - 2026
DA - 2026/09/03
JO - PLOS One
DO - 10.1371/journal.pone.0357267
UR - https://doi.org/10.1371/journal.pone.0357267
AB - The rapid growth of seismic waveform data requires efficient and elastic processing, but virtual-machine-based architectures often tie workload execution to coarse resource units and rigid scaling policies. To address these limitations, we propose a Kubernetes- and Docker-based cloud-native framework for real-time seismic waveform data quality assessment. The framework organizes the processing logic into three tiers: infrastructure and orchestration, elastic scaling policy, and data quality assessment services. It further introduces an Adaptive Elastic Scheduling Algorithm (AESA) that combines CPU and memory high-watermark triggers with task-lifecycle-aware contraction for containerized computation of key quality indicators, including gap count, gap duration, and percent of availability. Under equivalent hardware resource constraints, the cloud-native implementation reduced mean response latency by 47.27% at 20 requests per second and 47.26% at 50 requests per second compared with the VM-based control group, corresponding to an approximately 1.90-fold speedup in both load regimes. These results indicate that, for the tested gap-count, gap-duration, and availability workloads, cloud-native orchestration can improve latency without changing the quality-metric code. The evaluation is limited to the specified workload, resource budget, and two traffic levels.
SN - 1932-6203
ER -