{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "The Second Monitoring and Application Center, China Earthquake Administration, Xi’an, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yang Li"
    },
    {
      "name": "Danning Wang"
    },
    {
      "name": "Runyuan Dong"
    },
    {
      "name": "Chen Cheng"
    },
    {
      "name": "Xiuping Zhang"
    },
    {
      "name": "Wenqing Wang"
    },
    {
      "name": "Chuanjin Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1b2hxaj5rm6xt.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357267",
  "fulltext_url": null,
  "id": "1b2hxaj5rm6xt",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357267",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1710|Cloud Computing and Resource Management"
    ],
    [
      "plos",
      "/Computer and information sciences/Computer architecture/Computer hardware"
    ],
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      "/Computer and information sciences/Computing methods/Cloud computing"
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      "/Computer and information sciences/Real time computing"
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      "plos",
      "/Computer and information sciences/Systems science/Control systems"
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      "plos",
      "/Earth sciences/Geophysics/Seismology"
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      "/Engineering and technology/Industrial engineering/Control engineering/Control systems"
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      "/Engineering and technology/Industrial engineering/Quality control"
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      "/Physical sciences/Mathematics/Applied mathematics/Algorithms"
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      "plos",
      "/Physical sciences/Mathematics/Systems science/Control systems"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Geophysics/Seismology"
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      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357267&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1b2hxaj5rm6xt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pone.0357267",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357267"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "plos:seismology",
      "subfield": "geophysics"
    }
  ],
  "title": "A cloud-native framework for seismic waveform data quality assessment: Performance evaluation under equivalent resource constraints",
  "title_text": "A cloud-native framework for seismic waveform data quality assessment: Performance evaluation under equivalent resource constraints",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/1b2hxaj5rm6xt",
  "venue": "PLOS One",
  "versions": []
}