SequenceFI: Non-intrusive Temporal Fault Injection for Microservice Systems

2026-07-22

AI Summary

This work addresses the challenge of precisely controlling fault injection timing in distributed requests, which existing microservice fault injection approaches struggle to achieve, thereby failing to effectively reproduce timing-sensitive failures. The authors propose a non-intrusive, timing-aware fault injection framework that observes message-level send and receive events, propagates compact temporal evidence along call chains, and automatically synthesizes timing-based trigger conditions from trace data—enabling fine-grained fault injection without modifying application code. Implemented on Kubernetes, the framework supports non-intrusive deployment. Evaluation across four benchmark systems covering nine timing-sensitive fault scenarios and 450 experiments demonstrates 100% injection accuracy, with effective configurations generated in an average of one attempt and a 95.91% reduction in search time compared to H-Random.

Abstract

Fault injection is widely used to evaluate the resilience of microservice systems, where client requests often span multiple services and execution stages. Existing request-level techniques usually control where and what faults are injected, but not when they are activated within a distributed execution. This limitation makes it difficult to reproduce timing-dependent failures, such as failures after state-changing side effects, order-sensitive concurrent responses, and partial failures among repeated downstream calls. This paper presents SequenceFI, a non-intrusive framework for temporal fault injection in microservice systems. SequenceFI observes message-level send and receive events, propagates compact temporal evidence along request executions, and triggers faults only when occurrence-sensitive temporal guards are satisfied. It further synthesizes temporal guards from traces, reducing the need for exhaustive enumeration of temporal fault-injection configurations, while requiring no modifications to application code or serialization libraries. We implement SequenceFI on Kubernetes and evaluate it on four widely used microservice benchmarks. Across nine temporal-fault scenarios and 450 valid trials, SequenceFI achieves 100.0\% temporal success without premature or multiple injections, finds effective configurations in one attempt on average, and reduces aggregate end-to-end search time by 95.91\% compared with H-Random.

Problem

• temporal fault injection
• microservice systems
• timing-dependent failures
• resilience evaluation
• distributed execution

Innovation

• temporal fault injection
• microservice resilience
• non-intrusive
• temporal guards
• distributed tracing