Year: 2024
Type: conference-paper
Abstract: Fault injection ensures resilience of cloud-edge computing systems.Recognizing the importance of fault injection underscores a critical challenge: the need to enhance system observability for conducting effective and comprehensive fault injection experiments.This is particularly crucial due to the complex, distributed architectures of cloud-edge environments, where a single failure can trigger cascading failures, creating a dynamic and expanding fault blast radius.To address these challenges, this work advocates for the adoption of knowledge graph technologies to significantly improve fault knowledge representation, system observability, and minimize resource expenditure on monitoring systems.We introduce the Fault Observation Knowledge Graph (FOKG), comprising three detailed sub-knowledge graphs that describe the nuances of application deployment, delineate known fault chains, and outline the monitoring mechanisms in place.By extracting dynamic fault blast radius information and refining monitoring strategies from FOKG after fault injection, we ensure a more targeted observation and efficient fault injection experiment.Our methodology, validated on a custom-developed Open-Stack platform, surpasses traditional fault injection techniques by achieving a remarkable 42.76% reduction in the average time required to observe the complete fault blast radius.
Authors: Jun Ruan, Xiaohu Yang, Yuan Fan
Institutions: Alibaba Group (China), Zhejiang University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4406255385