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English(EN) BEACON: Bridging Multimodal Semantics and Asynchronous Propagation for Cloud System Anomaly Detection

BEACON框架通过多模态和异步分析增强云异常检测能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了BEACON，一个旨在改进大规模云系统异常检测的新框架。BEACON通过有效整合多模态数据（如指标和日志）并考虑系统实例之间的异步时间依赖性，解决了现有方法的局限性。该框架利用动态可解释多模态聚合器（DIMA）来对齐语义和时间信息，并利用异步时空传播器（ASTP）来建模空间和时间关系。实验表明，BEACON在真实云系统数据集上的表现优于当前最先进的方法。 AI

影响 通过增强异常检测能力，提高云系统的可靠性。

排序理由 该集群包含一篇详细介绍新异常检测框架的研究论文。

在 arXiv cs.LG 阅读 →

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BEACON框架通过多模态和异步分析增强云异常检测能力

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zhe Li, Tengxue Zhang, Yang Shu, Tung Kieu, Lunting Fan, Yingying Zhang, Bin Yang ·

    BEACON：为云系统异常检测架起多模态语义与异步传播的桥梁

    arXiv:2610.27542v1 Announce Type: new Abstract: Modern large-scale cloud systems continuously generate massive and multimodal observability data, such as time-series metrics, log records, and system dependency graphs, which reflect the health state of the system. To ensure reliab…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    BEACON: Bridging Multimodal Semantics and Asynchronous Propagation for Cloud System Anomaly Detection

    Modern large-scale cloud systems continuously generate massive and multimodal observability data, such as time-series metrics, log records, and system dependency graphs, which reflect the health state of the system. To ensure reliable cloud system operation, anomaly detection mod…

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