Service anomalies are difficult to locate accurately due to their propagation through service dependencies in microservice systems. Besides, the protection mechanisms are introduced into the microservice systems to ensure the stable operation of services. However, the existing approaches ignore the impact of protection mechanisms on the root cause localization of abnormal services. Specifically, the circuit breaking and rate limiting mechanisms can refuse service requests and thus change the way of anomaly propagation. Moreover, the different service request frequencies and latency make service dependencies change dynamically, resulting in the different probabilities of anomaly propagation among services. In this paper, we propose a novel framework named MicroGBPM to locate the root cause of abnormal services. We model the anomaly propagation among services as a dynamically constructed service attributed graph with metrics and traces when a failure occurs. To eliminate the impact of the protection mechanisms, we design a two-stage dynamic calibration strategy to adjust the probability of anomaly propagation among services. Then, we propose a random walking approach to calculate the root cause results by using the PageRank algorithm. The experimental results show that MicroGBPM improves the accuracy of root cause localization compared to other approaches in the microservice systems with protection mechanisms.

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Nowadays, the protection mechanisms are introduced into microservice systems to ensure the stable operation of services. However, existing approaches ignore the impact of protection mechanisms on the root cause localization of abnormal services. Specifically, the circuit breaking and rate limiting mechanisms can refuse service requests and thus change the way of anomaly propagation. Moreover, different service request frequencies and response time make service dependencies change dynamically, resulting in different probabilities of anomaly propagation among services. In this paper, we propose a novel framework named MicroGBPM to locate the root cause of abnormal services, which considers the impact of the protection mechanisms. We model anomaly propagation among services as a dynamically constructed service attributed graph with metrics and traces when a failure occurs. To eliminate the impact of the protection mechanisms, we design a two-stage dynamic calibration strategy to adjust the probability of anomaly propagation among services. Then we propose a random walking approach to calculate the root cause results by using the PageRank algorithm. The experimental results show that MicroGBPM improves the accuracy of root cause localization compared to other approaches in microservice systems with protection mechanisms.