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<p>Microservice has become a dominant approach for building large-scale Internet applications. The microservice-based system (MS) consists of thousands of services, and its complex interactions make it highly susceptible to unforeseen cascading failures. Cascading failure models are commonly used to analyze the system’s tolerance, while the existing models overlook MS’s features and fail to incorporate real-world events, leading to bias in simulation results. To address these, we proposed a comprehensive tolerance analysis framework of MS named the MSTAF. Specifically, we extracted the real-world failure-triggering scenarios and constructed the Workload-based Cascading Failure Model (WL-CFM) to model the load initialization and redistribution. Then, we implemented the Business Loss Assessment Method (BLAM) to quantify the impact by calculating the workload loss. To validate our MSTAF, we conducted experiments on the WL-CFM and BMAL and performed an analysis on the TrainTicket (TT). The results confirm the MSTAF’s superiority. Specifically, the WL-CFM outperforms baselines, reducing simulation error by 10– 48%. The BMAL demonstrates greater accuracy, with deviations from the ground truth ranging from <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(-45\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>-</mo> <mn>45</mn> </mrow> </math></EquationSource> </InlineEquation>% to + 7%. Overall, the MSTAF offers valuable insights for enhancing tolerance and provides an effective solution for developers and researchers.</p>

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A tolerance analysis framework for microservice-based systems against cascading failures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Chunyang Zheng,
  • Shuaizong Si,
  • Xiaoxi Wang,
  • Jinfa Wang,
  • Xiaofeng Zhang,
  • Shichao Lv,
  • Limin Sun

摘要

Microservice has become a dominant approach for building large-scale Internet applications. The microservice-based system (MS) consists of thousands of services, and its complex interactions make it highly susceptible to unforeseen cascading failures. Cascading failure models are commonly used to analyze the system’s tolerance, while the existing models overlook MS’s features and fail to incorporate real-world events, leading to bias in simulation results. To address these, we proposed a comprehensive tolerance analysis framework of MS named the MSTAF. Specifically, we extracted the real-world failure-triggering scenarios and constructed the Workload-based Cascading Failure Model (WL-CFM) to model the load initialization and redistribution. Then, we implemented the Business Loss Assessment Method (BLAM) to quantify the impact by calculating the workload loss. To validate our MSTAF, we conducted experiments on the WL-CFM and BMAL and performed an analysis on the TrainTicket (TT). The results confirm the MSTAF’s superiority. Specifically, the WL-CFM outperforms baselines, reducing simulation error by 10– 48%. The BMAL demonstrates greater accuracy, with deviations from the ground truth ranging from \(-45\) - 45 % to + 7%. Overall, the MSTAF offers valuable insights for enhancing tolerance and provides an effective solution for developers and researchers.