Abstract

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 …

Date

May 16, 2022

Authors

Yufeng Xin, Shih-Wen Fu, Anirban Mandal, Ryan Tanaka, Mats Rynge, Karan Vahi, Ewa Deelman

Conference

ICC 2022-IEEE International Conference on Communications

Pages

341-346

Publisher

IEEE