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This paper introduces SequenceFI, a non-intrusive framework designed for temporal fault injection in microservice systems, addressing the limitations of existing request-level techniques that fail to account for the timing of fault activations. By observing message-level events and synthesizing temporal guards from execution traces, SequenceFI enables precise fault injection that aligns with the timing of state changes and concurrent responses. The framework demonstrates a 100% success rate in reproducing temporal faults across various scenarios while significantly reducing the search time for effective configurations by nearly 96% compared to traditional methods.
SequenceFI achieves flawless temporal fault injection in microservices, cutting search time by nearly 96% while requiring no code modifications.
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.
