With the advantages of strong scalability and fast delivery, microser-vice has become a popular software architecture in the modern ITindustry. Most of microservice faults manifest themselves in terms of service latency increase and impact user experience. The explosion in the number of service instances and complex dependencies among them make the application diagnosis extremely challenging.To help understand and troubleshoot a microservice system, the end-to-end tracing technology has been widely applied to capturethe execution path of each request. However, the tracing data are not fully leveraged by cloud and application providers when con-ducting latency issue localization in the microservice environment.This paper proposes a novel system ,named MicroRank, which analyzes clues provided by normal and abnormal traces to locateroot causes of latency issues. Once a latency issue is detected by the Anomaly Detector in MicroRank, the cause localization procedure is triggered. MicroRank first distinguishs which traces are abnormal. Then, MicroRank’s PageRank Scorer module uses the abnormal and normal trace information as its input and differentials the importance of different traces to extended spectrum techniques . Finally, the spectrum techniques can calculate the ranking list based on the weighted spectrum information from PageRank Scorer to locate root causes more effectively. The experimental evaluationson a widely-used open-source system and a production system show that MicroRank achieves excellent results not only in one root cause situation but also in two issues that happen at the same time. Moreover,MicroRank makes 6% to 22% improvement in recall in localizing root causes compared to current state-of-the- art methods.

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