Data Integrity Error Localization in Networked Systems with Missing Data
Data Integrity Error Localization in Networked Systems with Missing Data
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DOI：
10.1109/icc45855.2022.9838996
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Yufeng Xin;Shi Fu;A. Mandal;Ryan Tanaka;M. Rynge;K. Vahi;E. Deelman
中科院分区：
文献类型：
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作者：
Yufeng Xin;Shi Fu;A. Mandal;Ryan Tanaka;M. Rynge;K. Vahi;E. Deelman
最近的网络故障诊断系统主要针对数据中心网络，可以部署复杂的测量系统来获取路由信息并确保网络覆盖范围，从而实现准确、快速的故障定位。在本文中，我们的目标是支持数据密集型分布式应用程序的广域网。我们首先提出了一种新的多输出预测模型，该模型直接映射应用程序级别的观察结果以定位系统组件故障。实际上，这种以应用程序为中心的方法可能面临数据丢失的挑战，因为推理模型的一些输入（特征）数据可能由于广域网中的测量不完整或丢失而丢失。我们表明，所提出的预测模型自然地允许多元插补来恢复丢失的数据。我们评估了多种插补算法，并表明在大规模网络中可以显着提高预测性能。据我们所知，这是第一个关于缺失数据问题以及在网络故障定位中应用插补技术的研究。
Most recent network failure diagnosis systems focused on data center networks where complex measurement systems can be deployed to derive routing information and ensure network coverage in order to achieve accurate and fast fault localization. In this paper, we target wide-area networks that support data-intensive distributed applications. We first present a new multi-output prediction model that directly maps the application level observations to localize the system component failures. In reality, this application-centric approach may face the missing data challenge as some input (feature) data to the inference models may be missing due to incomplete or lost measurements in wide area networks. We show that the presented prediction model naturally allows the multivariate imputation to recover the missing data. We evaluate multiple imputation algorithms and show that the prediction performance can be improved significantly in a large-scale network. As far as we know, this is the first study on the missing data issue and applying imputation techniques in network failure localization.