FastFI: Enhancing API Call-Site Robustness in Microservice-Based Systems with Fault Injection

TL;DR

FastFI tackles the exponential growth of combinatorial fault space in microservice fault injection by introducing a DFS-based solver that exploits the monotone, low-overlap CNF structure, coupled with dynamic fault injection to adaptively bound the search. It then derives an actionable robustness-optimization stage that casts API call-site hardening as a Partial Max-SAT problem, prioritizing high-impact call sites under budget. Across four real and simulated benchmarks, FastFI dramatically reduces end-to-end fault-injection time and CNF-solving time while accurately identifying high-risk APIs for hardening, demonstrating practical scalability in DevOps pipelines. The work offers a concrete pathway to systematically strengthen microservice reliability through targeted call-site policies, with potential for non-intrusive deployment in future work.

Abstract

Fault injection is a key technique for assessing software reliability, enabling proactive detection of system defects before they manifest in production. However, the increasing complexity of microservice architectures leads to exponential growth in the fault-injection space, rendering traditional random injection inefficient. Recent lineage-driven approaches mitigate this problem through heuristic pruning, but they face two limitations. First, combinatorial-fault discovery remains bottlenecked by general-purpose SAT solvers, which fail to exploit the monotone and low-overlap structure of derived CNF formulas and typically rely on a static upper bound on fault size. Second, existing techniques provide limited post-injection guidance beyond reporting detected faults. To address these challenges, we propose FastFI, a fault-injection-guided framework to enhance the robustness of API call sites in microservice-based systems. FastFI features a DFS-based solver with dynamic fault injection to discover all valid combinatorial faults, and it leverages fault-injection results to identify critical APIs whose call sites should be hardened for robustness. Experiments on four representative microservice benchmarks show that FastFI reduces end-to-end fault-injection time by an average of 76.12\% compared to state-of-the-art baselines while maintaining acceptable resource overhead. Moreover, FastFI accurately identifies high-impact APIs and provides actionable guidance for call-site hardening.

Figures (16)

• Figure 1: Illustration of fault injection.
• Figure 2:
• Figure 3: End-to-end workflow of combinatorial-fault injection.
• Figure 4: Combinatorial-fault discovery pipeline in Online Boutique.
• Figure 5: Example API call sites in the source code.

Theorems & Definitions (1)

• theorem 1: Time Complexity