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To tackle various performance problems among a large number of services and machines, an end\u2010to\u2010end tracing tool is always equipped in these systems to track the execution path of every single request. However, it is nontrivial to conduct root cause analysis of anomalies with such a large volume of tracing data. This paper proposes a novel system named\n                    <jats:italic>TraceRank<\/jats:italic>\n                    to identify and locate abnormal services causing performance problems with dis\u2010aggregated end\u2010to\u2010end traces.\n                    <jats:italic>TraceRank<\/jats:italic>\n                    mainly includes an anomaly detection module and a root cause analysis module. The root cause analysis procedure is triggered when an anomaly is detected. To fully leverage the information provided by the tracing data, both the spectrum analysis and the PageRank\u2010based random walk methods are introduced to pinpoint abnormal services. The experiments in TrainTicket and Bookinfo microservice benchmarks and a real\u2010world system show that\n                    <jats:italic>TraceRank<\/jats:italic>\n                    can locate root causes with 90% in Precision and 86% in Recall.\n                    <jats:italic>TraceRank<\/jats:italic>\n                    has up to 10% improvement compared with several state\u2010of\u2010the\u2010art approaches in both Precision and Recall. Finally,\n                    <jats:italic>TraceRank<\/jats:italic>\n                    has good scalability and a low overhead to adapt to large\u2010scale microservice systems.\n                  <\/jats:p>","DOI":"10.1002\/smr.2413","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T19:34:22Z","timestamp":1638905662000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["TraceRank: Abnormal service localization with dis\u2010aggregated end\u2010to\u2010end tracing data in cloud native systems"],"prefix":"10.1002","volume":"35","author":[{"given":"Guangba","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer and Engineering Sun Yat\u2010sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zicheng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer and Engineering Sun Yat\u2010sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-6900","authenticated-orcid":false,"given":"Pengfei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Engineering Sun Yat\u2010sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2016.64"},{"key":"e_1_2_11_3_1","doi-asserted-by":"crossref","unstructured":"YuG ChenP ZhengZ.Microscaler: automatic scaling for microservices with an online learning approach. 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