{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T07:29:50Z","timestamp":1771399790498,"version":"3.50.1"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:p>\n                    Cloud-native microservices improve development velocity and elasticity, but they also create complex and dynamic service dependencies. Resource contention, queue buildup, and downstream slowdowns can propagate through call chains, amplifying end-to-end tail latency (e.g., p95\/p99) and increasing Service Level Objective (SLO) violation risks. While many studies focus on\n                    <jats:italic>post-hoc<\/jats:italic>\n                    anomaly detection and root-cause analysis, industrial operations increasingly demand proactive capabilities, like predicting performance risks before a request finishes, issuing early warnings from partial trace prefixes, and producing actionable signals for mitigation. This mini-review synthesizes recent progress on trace-driven proactive SLO management. We summarize problem formulations and evaluation protocols for SLO violation and tail-quantile prediction, prefix early warning under precision constraints, and actionable intermediate outputs such as bottleneck candidate ranking and what-if estimation. We then survey modeling approaches spanning feature-based baselines, sequence models, graph neural networks, sequence-graph fusion, and multimodal\/causal extensions, highlighting practical issues such as class imbalance, sampling-induced missing spans, and topology drift. Finally, we survey commonly used public benchmarks and traces, and discuss open challenges toward deployable, trustworthy proactive SLO management.\n                  <\/jats:p>","DOI":"10.3389\/fcomp.2026.1783945","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T06:55:31Z","timestamp":1771397731000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From distributed tracing to proactive SLO management: a mini-review of trace-driven performance prediction for cloud-native microservices"],"prefix":"10.3389","volume":"8","author":[{"given":"Miaopeng","family":"Yu","sequence":"first","affiliation":[{"name":"Electric Power Research Institute, CSG","place":["Guangzhou, China"]},{"name":"Guangdong Provincial Key Laboratory of Power System Network Security","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Liu","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, CSG","place":["Guangzhou, China"]},{"name":"Guangdong Provincial Key Laboratory of Power System Network Security","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinran","family":"Du","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, CSG","place":["Guangzhou, China"]},{"name":"Guangdong Provincial Key Laboratory of Power System Network Security","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kequan","family":"Lin","sequence":"additional","affiliation":[{"name":"China Southern Power Grid","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Dai","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, CSG","place":["Guangzhou, China"]},{"name":"Guangdong Provincial Key Laboratory of Power System Network Security","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzhe","family":"Fu","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, CSG","place":["Guangzhou, China"]},{"name":"Guangdong Provincial Key Laboratory of Power System Network Security","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyan","family":"Yang","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, CSG","place":["Guangzhou, China"]},{"name":"Guangdong Provincial Key Laboratory of Power System Network Security","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1803.01271","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv preprint arXiv:1803.01271"},{"key":"B2","volume-title":"Site Reliability Engineering: How Google Runs Production Systems","author":"Beyer","year":"2016"},{"key":"B3","doi-asserted-by":"publisher","first-page":"103465","DOI":"10.1016\/j.ipm.2023.103465","article-title":"Calimera: a new early time series classification method","volume":"60","author":"Bilski","year":"2023","journal-title":"Inform. Process. Manag"},{"key":"B4","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/ICWS62655.2024.00155","article-title":"\u201cQueueflower: orchestrating microservice workflows via dynamic queue balancing,\u201d","volume-title":"2024 IEEE International Conference on Web Services (ICWS)","author":"Cao","year":"2024"},{"key":"B5","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.comcom.2023.03.028","article-title":"TraceGra: a trace-based anomaly detection for microservice using graph deep learning","volume":"204","author":"Chen","year":"2023","journal-title":"Comput. Commun."},{"key":"B6","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"\u201cXgboost: a scalable tree boosting system,\u201d","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'16","author":"Chen","year":"2016"},{"key":"B7","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.3115\/v1\/D14-1179","article-title":"\u201cLearning phrase representations using rnn encoder\u2013decoder for statistical machine translation,\u201d","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Cho","year":"2014"},{"key":"B8","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1145\/3132747.3132772","article-title":"\u201cResource central: understanding and predicting workloads for improved resource management in large cloud platforms,\u201d","volume-title":"Proceedings of the 26th Symposium on Operating Systems Principles, SOSP'17","author":"Cortez","year":"2017"},{"key":"B9","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1145\/2408776.2408794","article-title":"The tail at scale","volume":"56","author":"Dean","year":"2013","journal-title":"Commun. ACM"},{"key":"B10","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/978-3-319-67425-4_12","author":"Dragoni","year":"2017","journal-title":"Microservices: Yesterday, Today, and Tomorrow"},{"key":"B11","first-page":"1285","article-title":"\u201cDeeplog: anomaly detection and diagnosis from system logs through deep learning,\u201d","volume-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS'17","author":"Du","year":"2017"},{"key":"B12","first-page":"135","article-title":"\u201cSage: practical and scalable ml-driven performance debugging in microservices,\u201d","volume-title":"Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS'21","author":"Gan","year":"2021"},{"key":"B13","first-page":"3","article-title":"\u201cAn open-source benchmark suite for microservices and their hardware-software implications for cloud &edge systems,\u201d","volume-title":"Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS'19","author":"Gan","year":"2019"},{"key":"B14","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1109\/ICCNC.2014.6785329","article-title":"\u201cImproving qos in real-time internet applications: from best-effort to software-defined networks,\u201d","volume-title":"2014 International Conference on Computing, Networking and Communications (ICNC)","author":"Gorlatch","year":"2014"},{"key":"B15","first-page":"165","article-title":"\u201cSuanming: explainable prediction of performance degradations in microservice applications,\u201d","volume-title":"Proceedings of the ACM\/SPEC International Conference on Performance Engineering, ICPE'21","author":"Grohmann","year":"2021"},{"key":"B16","first-page":"1321","article-title":"\u201cOn calibration of modern neural networks,\u201d","volume-title":"International Conference on Machine Learning","author":"Guo","year":"2017"},{"key":"B17","doi-asserted-by":"publisher","first-page":"102376","DOI":"10.1016\/j.peva.2023.102376","article-title":"Pobo: safe and optimal resource management for cloud microservices","volume":"162","author":"Guo","year":"2023","journal-title":"Perform. Eval"},{"key":"B18","first-page":"1025","article-title":"\u201cInductive representation learning on large graphs,\u201d","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17","author":"Hamilton","year":"2017"},{"key":"B19","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/ISSRE.2016.21","article-title":"\u201cExperience report: system log analysis for anomaly detection,\u201d","volume-title":"2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE)","author":"He","year":"2016"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1109\/TNNLS.2020.3027736","article-title":"A spatiotemporal deep learning approach for unsupervised anomaly detection in cloud systems","volume":"34","author":"He","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"B22","first-page":"276","article-title":"\u201cSystemizing and mitigating topological inconsistencies in alibaba's microservice call-graph datasets,\u201d","volume-title":"Proceedings of the 15th ACM\/SPEC International Conference on Performance Engineering, ICPE'24","author":"Huye","year":"2024"},{"key":"B23","first-page":"3149","article-title":"\u201cLightgbm: a highly efficient gradient boosting decision tree,\u201d","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17","author":"Ke","year":"2017"},{"key":"B24","author":"Kipf","year":"2017","journal-title":"Semi-supervised classification with graph convolutional networks"},{"key":"B25","doi-asserted-by":"publisher","first-page":"8","DOI":"10.13648\/j.cnki.issn1674-0629.2016.06.002","article-title":"A new generation of power dispatching automation system based on cloud computing architecture","volume":"10","author":"Liang","year":"2016","journal-title":"South. Power Syst. Technol"},{"key":"B26","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/CLOUD67622.2025.00024","article-title":"\u201cCausal latency modelling for cloud microservices,\u201d","volume-title":"2025 IEEE 18th International Conference on Cloud Computing (CLOUD)","author":"Lohse","year":"2025"},{"key":"B27","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1145\/3472883.3487003","article-title":"\u201cCharacterizing microservice dependency and performance: Alibaba trace analysis,\u201d","volume-title":"Proceedings of the ACM Symposium on Cloud Computing, SoCC'21","author":"Luo","year":"2021"},{"key":"B28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1145\/3542929.3563477","article-title":"\u201cThe power of prediction: microservice auto scaling via workload learning,\u201d","volume-title":"Proceedings of the 13th Symposium on Cloud Computing, SoCC'22","author":"Luo","year":"2022"},{"key":"B29","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/INFCOMW.2014.6849255","article-title":"\u201cBringing mobile online games to clouds,\u201d","volume-title":"2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","author":"Meil\u00e4nder","year":"2014"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1016\/j.future.2017.07.041","article-title":"Modeling the scalability of real-time online interactive applications on clouds","volume":"86","author":"Meil\u00e4nder","year":"2018","journal-title":"Fut. Gen. Comput. Syst"},{"key":"B31","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/ASE56229.2023.00038","article-title":"\u201cDeepscaler: holistic autoscaling for microservices based on spatiotemporal gnn with adaptive graph learning,\u201d","volume-title":"2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE)","author":"Meng","year":"2023"},{"key":"B32","article-title":"AzurePublicDatasetV1","year":"","journal-title":"GitHub Repository"},{"key":"B33","year":"","journal-title":"AzurePublicDatasetV2. GitHub Repository"},{"key":"B34","volume-title":"Building Microservices: Designing Fine-Grained Systems","author":"Newman","year":"2021"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3483424","article-title":"A survey of aiops methods for failure management","volume":"12","author":"Notaro","year":"2021","journal-title":"ACM Trans. Intell. Syst. Technol"},{"key":"B36","year":"2025","journal-title":"OpenTelemetry Specification 1.52.0"},{"key":"B37","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1145\/3485983.3494866","article-title":"\u201cGraf: a graph neural network based proactive resource allocation framework for slo-oriented microservices,\u201d","volume-title":"Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, CoNEXT'21","author":"Park","year":"2021"},{"key":"B38","doi-asserted-by":"publisher","first-page":"3331","DOI":"10.1109\/TNET.2024.3393427","article-title":"Graph neural network-based slo-aware proactive resource autoscaling framework for microservices","volume":"32","author":"Park","year":"2024","journal-title":"IEEE\/ACM Trans. Netw"},{"key":"B39","first-page":"6639","article-title":"\u201cCatboost: unbiased boosting with categorical features,\u201d","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18","author":"Prokhorenkova","year":"2018"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2508.01635","article-title":"Learning unified system representations for microservice tail latency prediction","author":"Qian","year":"2025","journal-title":"arXiv preprint"},{"key":"B41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2391229.2391236","article-title":"\u201cHeterogeneity and dynamicity of clouds at scale: Google trace analysis,\u201d","volume-title":"Proceedings of the Third ACM Symposium on Cloud Computing, SOCC'12","author":"Reiss","year":"2012"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1007\/s10618-020-00690-z","article-title":"Teaser: early and accurate time series classification","volume":"34","author":"Sch\u00e4fer","year":"2020","journal-title":"Data Min. Knowl. Discov"},{"key":"B43","volume-title":"Dapper, a Large-Scale Distributed Systems Tracing Infrastructure","author":"Sigelman","year":"2010"},{"key":"B44","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1145\/3580305.3599465","article-title":"\u201cPert-gnn: latency prediction for microservice-based cloud-native applications via graph neural networks,\u201d","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD'23","author":"Tam","year":"2023"},{"key":"B45","doi-asserted-by":"publisher","first-page":"20787","DOI":"10.1609\/aaai.v39i19.34291","article-title":"Fastpert: Towards fast microservice application latency prediction via structural inductive bias over pert networks","volume":"39","author":"Tam","year":"2025","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"B46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3342195.3387517","article-title":"\u201cBorg: the next generation,\u201d","volume-title":"Proceedings of the Fifteenth European Conference on Computer Systems, EuroSys'20","author":"Tirmazi","year":"2020"},{"key":"B47","first-page":"6000","article-title":"\u201cAttention is all you need,\u201d","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17","author":"Vaswani","year":"2017"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1710.10903","article-title":"Graph attention networks","author":"Veli\u0109kovi\u0107","year":"2017","journal-title":"arXiv preprint"},{"key":"B49","year":"2023","journal-title":"Sock Shop: A Microservice Demo Application"},{"key":"B50","doi-asserted-by":"publisher","first-page":"15","DOI":"10.13648\/j.cnki.issn1674-0629.2016.06.003","article-title":"Architecture design of an intelligent dispatching supporting platform based on integration of core business","volume":"10","author":"Wen","year":"2016","journal-title":"South. Power Syst. Technol"},{"key":"B51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/NOMS47738.2020.9110353","article-title":"\u201cMicrorca: root cause localization of performance issues in microservices,\u201d","author":"Wu","year":"2020","journal-title":"NOMS 2020"},{"key":"B52","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10115-011-0400-x","article-title":"Early classification on time series","volume":"31","author":"Xing","year":"2012","journal-title":"Knowl. Inform. Syst"},{"key":"B53","doi-asserted-by":"crossref","first-page":"3087","DOI":"10.1145\/3442381.3449905","article-title":"\u201cMicrorank: end-to-end latency issue localization with extended spectrum analysis in microservice environments,\u201d","volume-title":"Proceedings of the Web Conference 2021, WWW'21","author":"Yu","year":"2021"},{"key":"B54","first-page":"167","article-title":"\u201cSinan: Ml-based and qos-aware resource management for cloud microservices,\u201d","volume-title":"Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS'21","author":"Zhang","year":"2021"},{"key":"B55","first-page":"655","article-title":"\u201cCRISP: critical path analysis of Large-Scale microservice architectures,\u201d","volume-title":"2022 USENIX Annual Technical Conference (USENIX ATC 22)","author":"Zhang","year":"2022"},{"key":"B56","doi-asserted-by":"crossref","first-page":"5639","DOI":"10.1145\/3580305.3599902","article-title":"\u201cRobust multimodal failure detection for microservice systems,\u201d","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD'23","author":"Zhao","year":"2023"},{"key":"B57","doi-asserted-by":"publisher","first-page":"11106","DOI":"10.1609\/aaai.v35i12.17325","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","volume":"35","author":"Zhou","year":"","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"B58","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1109\/TSE.2018.2887384","article-title":"Fault analysis and debugging of microservice systems: industrial survey, benchmark system, and empirical study","volume":"47","author":"Zhou","year":"","journal-title":"IEEE Trans. Softw. Eng"}],"container-title":["Frontiers in Computer Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcomp.2026.1783945\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T06:55:39Z","timestamp":1771397739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcomp.2026.1783945\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,18]]},"references-count":58,"alternative-id":["10.3389\/fcomp.2026.1783945"],"URL":"https:\/\/doi.org\/10.3389\/fcomp.2026.1783945","relation":{},"ISSN":["2624-9898"],"issn-type":[{"value":"2624-9898","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,18]]},"article-number":"1783945"}}