Publication

Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

ACM SIGMETRICS


Abstract

Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, software, and tooling logs are often maintained separately, making it difficult to review the logs jointly for understanding production issues. Another challenge in reviewing the logs for identifying issues is the scale – there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability.

We first explore item-sets, i.e. combinations of feature values, that could identify groups of samples with sufficient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. These algorithms were designed for frequent item-set mining and association rule learning over transactional databases. After applying them on structured logs, we select the item-sets that are most unique to the target failures based on lift. We propose pre-processing steps with the use of a large-scale real-time database and post-processing techniques and parallelism to further speed up the analysis and improve interpretability, and demonstrate that such optimization is necessary for handling large-scale production datasets. We have successfully rolled out this approach for root cause investigation purposes in a large-scale infrastructure. We also present the setup and results from multiple production use cases in this paper.

Related Publications

All Publications

NeurIPS - December 6, 2020

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy

Innovative Technology at the Interface of Finance and Operations - March 31, 2021

Market Equilibrium Models in Large-Scale Internet Markets

Christian Kroer, Nicolas E. Stier-Moses

IMC - October 21, 2019

Internet Performance from Facebook’s Edge

Brandon Schlinker, Italo Cunha, Yi-Ching Chiu, Srikanth Sundaresan, Ethan Katz-Bassett

CC - March 3, 2021

Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization

Maksim Panchenko, Rafael Auler, Laith Sakka, Guilherme Ottoni

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy