CAREER: Towards Gray-Fault Tolerant Cloud through Harnessing and Enhancing System Observability

Johns Hopkins University, Baltimore MD

Investigators

Abstract

Cloud systems are the crucial infrastructure to many services existing today. Ensuring cloud software runs continuously without disruptions is both vital and challenging. Decades of research have developed mature techniques to detect and mask faults in distributed systems. But these techniques often use a simple model that assumes a system component either works or completely stops. Numerous real-world cloud incidents, however, suggest that production cloud systems frequently experience gray failures---a degraded operational mode in which a system component appears to be working but is in fact severely impaired. Gray failures cannot be effectively dealt with by current solutions. The overall objective of this proposal is to develop a holistic approach to detect, pinpoint and diagnose gray failures in production cloud systems. To realize the objective, four synergistic research activities are proposed. Specifically, the project conducts a study on real-world gray failure cases in popular distributed systems, measure and characterize the observability of existing systems. The project then designs a novel hybrid analysis that automatically inserts report-generation hooks across the whole systems stack to harness observability for detecting gray failures. To pinpoint the culprit component, this project further proposes algorithms to infer causality from the collected observations. Lastly, this project designs a runtime checking framework for increasing observability and online diagnosis of gray failures. Gray failures are a common cause of cloud service outages, resulting in significant financial loss. This project can effectively improve our understandings of gray failures and help detect and debug gray failures to reduce their impact on the ubiquitous cloud infrastructures. Software is moving to be more distributed with increasing subtle failure modes. Observability, fault detection, and localization are critical skills for this paradigm shift but are rarely covered in the existing curriculum. This project addresses this educational gap through curriculum development and student training. This project also promotes Computer Science education to underrepresented Baltimore high school students by organizing workshops in partnership with a non-profit organization, Code in the Schools, for local high school students to showcase cloud and system failure concepts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.