缺失数据网络系统的数据完整性错误定位
Data Integrity Error Localization in Networked Systems with Missing Data
摘要：
最近的网络故障诊断系统集中在数据中心网络，其中可以部署复杂的测量系统来获得路由信息并确保网络覆盖，以实现准确和快速的故障定位。在本文中，我们的目标广域网，支持数据密集型分布式应用程序。首先，我们提出了一个新的多输出预测模型，直接映射的应用程序级的意见，以本地化的系统组件故障。实际上，这种以应用为中心的方法可能面临数据缺失的挑战，因为由于广域网中的测量不完整或丢失，推断模型的一些输入（特征）数据可能缺失。我们表明，所提出的预测模型自然允许{ \it多元}插补恢复缺失的数据。我们评估了多个填补算法，并表明在大规模网络中的预测性能可以显着提高。据我们所知，这是第一次研究缺失数据问题，并将插补技术应用于网络故障定位。
英文摘要：
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 {\it 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
