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CSR: Small: Practical methods for removing latent configuration errors in cloud platforms

$450,000FY2015CSENSF

University Of California-San Diego, La Jolla CA

Investigators

Abstract

Configuration errors are a major cause for computer failures. A special type of configuration error called latent configuration errors, often has the highest severity and causes many serious, wide spread outages in data centers and cloud infrastructures, which affects millions of customers. These configuration errors are prevalent and expensive to troubleshoot, and can result in millions of dollars in business losses. This project addresses this important latent configuration problem that has caused many data center-wide outages in various cloud platforms. The methods developed by this project will significantly reduce the amount of severe data center-wide outages and improving the availability of cloud services and applications. Building on previous research experience in studying thousands of real world configuration errors in data centers, this project tackles this important latent configuration error problem via three practical and innovative research thrusts: (1) Automatically build configuration checkers to detect latent configuration errors at early stage before rolling out to thousands of nodes in data centers; (2) Design and build on-site configuration validation utility to allow data center administrators easily validate their configuration settings, especially those complex, latent ones; and (3) Improve configuration design to make them less prone to errors. The first research thrust observes the hidden validation checks in usages and develops automatic ways to separate the checks from the latent usages. The second research thrust is more fundamental as it aims to systematically simplify the configuration space to reduce configuration errors. The third thrust enables data center administrators to have more control of their configurations. In addition, various educational and outreach activities for students, especially women students in computer science.

View original record on NSF Award Search →