Fetching the paper…
Reading the bibliography…
The complexity and dynamism of microservices pose significant challenges to system reliability, and thereby, automated troubleshooting is crucial.
A. G. Hawkes, “Markov processes in APL,” in Conference Proceedings on APL 90: For the Future, APL 1990, Copenhagen, Denmark, August 13-17, 1990 . ACM, 1990, pp. 173–185
1990
Earlier work this paper cites.
S. P. Uselton, L. Treinish, J. P. Ahrens, E. W. Bethel, and A. State, “Multi-source data analysis challenges,” in 9th IEEE Visualization Conference, IEEE Vis 1998, Research Triangle Park, North Carolina, USA, October 18-23, 1998, Proceedings . IEEE Computer Society and ACM, 1998, pp. 501–504
1998
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
N. Marwede, M. Rohr, A. van Hoorn, and W. Hasselbring, “Automatic failure diagnosis support in distributed large-scale software systems based on timing behavior anomaly correlation,” in 13th European Conference on Software Maintenance and Reengineering, CSMR 2009, Architecture-Centric Maintenance of Large-SCale Software Systems, Kaiserslautern, Germany, 24-27 March 2009 . IEEE Computer Society, 2009, pp. 47–58
2009
Earlier work this paper cites.
S. Kandula, R. Mahajan, P. Verkaik, S. Agarwal, J. Padhye, and P. Bahl, “Detailed diagnosis in enterprise networks,” in Proceedings of the ACM SIGCOMM 2009 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, Barcelona, Spain, August 16-21, 2009 . ACM, 2009, pp. 243–254
2009
Earlier work this paper cites.
T. Fu, “A review on time series data mining,” Eng. Appl. Artif. Intell. , vol. 24, no. 1, pp. 164–181, 2011
2011
Earlier work this paper cites.
M. Kim, R. Sumbaly, and S. Shah, “Root cause detection in a service-oriented architecture,” in ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’13, Pittsburgh, PA, USA, June 17-21, 2013 . ACM, 2013, pp. 93–104
2013
Earlier work this paper cites.
K. Zhou, H. Zha, and L. Song, “Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes,” in Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA, April 29 - May 1, 2013 , vol. 31. JMLR.org, 2013, pp. 641–649. [Online]. Available: http://proceedings.mlr.press/v31/zhou13a.html
2013
Earlier work this paper cites.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014 . IEEE Computer Society, 2014, pp. 1725–1732
2014
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015 , ser. JMLR Workshop and Conference Proceedings, vol. 37. JMLR.org, 2015, pp. 448–456
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. Lea, R. Vidal, A. Reiter, and G. D. Hager, “Temporal convolutional networks: A unified approach to action segmentation,” in Computer Vision - ECCV 2016 Workshops - Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III , ser. Lecture Notes in Computer Science, vol. 9915, 2016, pp. 47–54
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Pham, L. Wang, B. Tak, S. Baset, C. Tang, Z. T. Kalbarczyk, and R. K. Iyer, “Failure diagnosis for distributed systems using targeted fault injection,” IEEE Trans. Parallel Distributed Syst. , vol. 28, no. 2, pp. 503–516, 2017
2017
Earlier work this paper cites.
P. He, J. Zhu, Z. Zheng, and M. R. Lyu, “Drain: An online log parsing approach with fixed depth tree,” in 2017 IEEE International Conference on Web Services, ICWS 2017, Honolulu, HI, USA, June 25-30, 2017 , I. Altintas and S. Chen, Eds. IEEE, 2017, pp. 33–40
2017
Earlier work this paper cites.
A. Siffer, P. Fouque, A. Termier, and C. Largouët, “Anomaly detection in streams with extreme value theory,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017 . ACM, 2017, pp. 1067–1075
2017
Earlier work this paper cites.
E. Bacry, M. Bompaire, S. Gaïffas, and S. Poulsen, “tick: a Python library for statistical learning, with a particular emphasis on time-dependent modeling,” ArXiv e-prints , Jul. 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier, “Language modeling with gated convolutional networks,” in Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 , ser. Proceedings of Machine Learning Research, vol. 70. PMLR, 2017, pp. 933–941
2017
Earlier work this paper cites.
M. Du, F. Li, G. Zheng, and V. Srikumar, “Deeplog: Anomaly detection and diagnosis from system logs through deep learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017 . ACM, 2017, pp. 1285–1298
2017
Earlier work this paper cites.
P. Wang, J. Xu, M. Ma, W. Lin, D. Pan, Y. Wang, and P. Chen, “Cloudranger: Root cause identification for cloud native systems,” in 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018, Washington, DC, USA, May 1-4, 2018 . IEEE Computer Society, 2018, pp. 492–502
2018
Earlier work this paper cites.
X. Zhou, X. Peng, T. Xie, J. Sun, C. Xu, C. Ji, and W. Zhao, “Benchmarking microservice systems for software engineering research,” in Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, ICSE 2018, Gothenburg, Sweden, May 27 - June 03, 2018 . ACM, 2018, pp. 323–324
2018
Earlier work this paper cites.
J. Lin, P. Chen, and Z. Zheng, “Microscope: Pinpoint performance issues with causal graphs in micro-service environments,” in Service-Oriented Computing - 16th International Conference, ICSOC 2018, Hangzhou, China, November 12-15, 2018, Proceedings , ser. Lecture Notes in Computer Science, vol. 11236. Springer, 2018, pp. 3–20
2018
Earlier work this paper cites.
A. Reinhart, “A review of self-exciting spatio-temporal point processes and their applications,” Statistical Science , vol. 33, no. 3, pp. 299–318, 2018
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
D. Beck, G. Haffari, and T. Cohn, “Graph-to-sequence learning using gated graph neural networks,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers . Association for Computational Linguistics, 2018, pp. 273–283
2018
Cited alongside, same era.
S. Nedelkoski, J. Cardoso, and O. Kao, “Anomaly detection from system tracing data using multimodal deep learning,” in 12th IEEE International Conference on Cloud Computing, CLOUD 2019, Milan, Italy, July 8-13, 2019 . IEEE, 2019, pp. 179–186
2019
Cited alongside, same era.
D. Liu, C. He, X. Peng, F. Lin, C. Zhang, S. Gong, Z. Li, J. Ou, and Z. Wu, “Microhecl: High-efficient root cause localization in large-scale microservice systems,” in 43rd IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice, ICSE (SEIP) 2021, Madrid, Spain, May 25-28, 2021 . IEEE, 2021, pp. 338–347
2021
Later among the works it cites.
S. He, P. He, Z. Chen, T. Yang, Y. Su, and M. R. Lyu, “A survey on automated log analysis for reliability engineering,” ACM Comput. Surv. , vol. 54, no. 6, pp. 130:1–130:37, 2021
2021
Later among the works it cites.
Z. Li, J. Chen, R. Jiao, N. Zhao, Z. Wang, S. Zhang, Y. Wu, L. Jiang, L. Yan, Z. Wang, Z. Chen, W. Zhang, X. Nie, K. Sui, and D. Pei, “Practical root cause localization for microservice systems via trace analysis,” in 29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021, Tokyo, Japan, June 25-28, 2021 . IEEE, 2021, pp. 1–10
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Zhou, X. Peng, T. Xie, J. Sun, C. Ji, D. Liu, Q. Xiang, and C. He, “Latent error prediction and fault localization for microservice applications by learning from system trace logs,” in Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 26-30, 2019 . ACM, 2019, pp. 683–694
2019
Cited alongside, same era.
M. M. Murray, A. Thelen, S. Ionta, and M. T. Wallace, “Contributions of intraindividual and interindividual differences to multisensory processes,” J. Cogn. Neurosci. , vol. 31, no. 3, 2019
2019
Cited alongside, same era.
Y. Gan, Y. Zhang, D. Cheng, A. Shetty, P. Rathi, N. Katarki, A. Bruno, J. Hu, B. Ritchken, B. Jackson, K. Hu, M. Pancholi, Y. He, B. Clancy, C. Colen, F. Wen, C. Leung, S. Wang, L. Zaruvinsky, M. Espinosa, R. Lin, Z. Liu, J. Padilla, and C. Delimitrou, “An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems,” in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2019, Providence, RI, USA, April 13-17, 2019 . ACM, 2019, pp. 3–18
2019
Cited alongside, same era.
H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. Muller, “Deep learning for time series classification: a review,” Data Min. Knowl. Discov. , vol. 33, no. 4, pp. 917–963, 2019
2019
Cited alongside, same era.
W. Meng, Y. Liu, Y. Zhu, S. Zhang, D. Pei, Y. Liu, Y. Chen, R. Zhang, S. Tao, P. Sun, and R. Zhou, “Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019 . ijcai.org, 2019, pp. 4739–4745
2019
Cited alongside, same era.
X. Zhang, Y. Xu, Q. Lin, B. Qiao, H. Zhang, Y. Dang, C. Xie, X. Yang, Q. Cheng, Z. Li, J. Chen, X. He, R. Yao, J. Lou, M. Chintalapati, F. Shen, and D. Zhang, “Robust log-based anomaly detection on unstable log data,” in Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 26-30, 2019 . ACM, 2019, pp. 807–817
2019
Cited alongside, same era.
H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang, “Time-series anomaly detection service at microsoft,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019 . ACM, 2019, pp. 3009–3017
2019
Cited alongside, same era.
Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, “Robust anomaly detection for multivariate time series through stochastic recurrent neural network,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019 . ACM, 2019, pp. 2828–2837
2019
Cited alongside, same era.
Y. Gan, Y. Zhang, K. Hu, D. Cheng, Y. He, M. Pancholi, and C. Delimitrou, “Seer: Leveraging big data to navigate the complexity of performance debugging in cloud microservices,” in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2019, Providence, RI, USA, April 13-17, 2019 . ACM, 2019, pp. 19–33
2019
Cited alongside, same era.
G. Yu, P. Chen, H. Chen, Z. Guan, Z. Huang, L. Jing, T. Weng, X. Sun, and X. Li, “Microrank: End-to-end latency issue localization with extended spectrum analysis in microservice environments,” in WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021 . ACM / IW3C2, 2021, pp. 3087–3098
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Yang, J. Shen, Y. Su, X. Ling, Y. Yang, and M. R. Lyu, “AID: efficient prediction of aggregated intensity of dependency in large-scale cloud systems,” in 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, Melbourne, Australia, November 15-19, 2021 . IEEE, 2021, pp. 653–665
2021
Later among the works it cites.
M. Marucci, G. Di Flumeri, G. Borghini, N. Sciaraffa, M. Scandola, E. F. Pavone, F. Babiloni, V. Betti, and P. Aricò, “The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence,” Scientific Reports , vol. 11, no. 1, p. 4831, Mar. 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Li, Y. Zhao, J. Han, Y. Su, R. Jiao, X. Wen, and D. Pei, “Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding,” in KDD ’21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021 . ACM, 2021, pp. 3220–3230
2021
Later among the works it cites.
Y. Gan, M. Liang, S. Dev, D. Lo, and C. Delimitrou, “Sage: practical and scalable ml-driven performance debugging in microservices,” in ASPLOS ’21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Virtual Event, USA, April 19-23, 2021 . ACM, 2021, pp. 135–151
2021
Later among the works it cites.
C. Zhang, X. Peng, C. Sha, K. Zhang, Z. Fu, X. Wu, Q. Lin, and D. Zhang, “Deeptralog: Trace-log combined microservice anomaly detection through graph-based deep learning,” in 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 2022, pp. 623–634
2022
Later among the works it cites.
Apache. (2022) Apache thrift. [Online]. Available: https://thrift.apache.org/
2022
Later among the works it cites.
C. N. C. Foundation. (2022) Jaeger. [Online]. Available: https://www.jaegertracing.io/
2022
Later among the works it cites.
Google. (2022) Container advisor. [Online]. Available: https://github.com/google/cadvisor
2022
Later among the works it cites.
C. N. C. Foundation. (2022) Prometheus. [Online]. Available: https://prometheus.io/
2022
Later among the works it cites.
InfluxData. (2022) Influxdb. [Online]. Available: https://www.influxdata.com/
2022
Later among the works it cites.
Elastic. (2022) Elasticsearch. [Online]. Available: https://www.elastic.co/
2022
Later among the works it cites.
S. Furuhashi. (2022) Fluentd. [Online]. Available: https://www.fluentd.org/architecture
2022
Later among the works it cites.
Elastic. (2022) Kibana. [Online]. Available: https://www.elastic.co/cn/kibana/
2022
Later among the works it cites.
Alibaba. (2022) Chaosblade. [Online]. Available: https://github.com/chaosblade-io/chaosblade
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Lee, T. Yang, Z. Chen, Y. Su, Y. Yang, and M. R. Lyu, “Heterogeneous anomaly detection for software systems via semi-supervised cross-modal attention,” 2022
2022
Later among the works it cites.