Collaborative and Observability Enhanced Fault Injection for Resilient Cloud-Edge Computing

Fault injection ensures the resilience of cloud-edge computing systems. System observability for conducting effective and comprehensive fault injection experiments must be enhanced, particularly due to the complex and distributed architectures of cloud-edge environments, where a single failure can trigger cascading effects, creating a dynamic and expanding fault blast radius. Additionally, accurate fault diagnosis becomes even more challenging when multiple teams simultaneously inject faults into interdependent components, highlighting the need for collaborative fault injection rather than confounding concurrent activities. This work addresses these challenges by proposing a collaborative fault injection framework powered by knowledge graph technologies. The framework enhances collaboration by enabling teams to seamlessly synchronize fault injection experiments within a shared environment. By extracting dynamic fault blast radius information from the proposed fault observation knowledge graph after fault injection, the system observability is improved and more efficient fault injection experiments are ensured. To evaluate the effectiveness of the proposed framework, we use an evolutionary game theory model to analyze the dynamic interactions among stakeholders. Utilizing a simulated OpenStack platform as our testbed, we demonstrate that our approach surpasses traditional fault injection techniques, achieving a remarkable 42.76% reduction in the average time required to observe the complete fault blast radius.