Fetching the paper…
Reading the bibliography…
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between the research areas of machine learning, big data, streaming analytics, and the management of IT operations.
Crameri, O., Knezevic, N., Kostic, D., Bianchini, R., Zwaenepoel, W.: Staged deployment in mirage, an integrated software upgrade testing and distribution system. ACM SIGOPS Operating Systems Review 41
2007
Earlier work this paper cites.
Doelitzscher, F., Knahl, M., Reich, C., Clarke, N.: Anomaly detection in iaas clouds. In: In the Proceedings of the 5th IEEE International Conference on Cloud Computing Technology and Science. vol. 1, pp. 387–394 (2013)
2013
Earlier work this paper cites.
Yuan, D., Luo, Y., Zhuang, X., Rodrigues, G.R., Zhao, X., Zhang, Y., Jain, P.U., Stumm, M.: Simple testing can prevent most critical failures: An analysis of production failures in distributed data-intensive systems. In: 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14). pp. 249–265 (2014)
2014
Earlier work this paper cites.
Zhang, S., Liu, Y., Pei, D., Chen, Y., Qu, X., Tao, S., Zang, Z.: Rapid and robust impact assessment of software changes in large internet-based services. In: Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies. pp. 1–13 (2015)
2015
Earlier work this paper cites.
Du, M., Li, F., Zheng, G., Srikumar, V.: Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 Conference on Computer and Communications Security (ACM SIGSAC). pp. 1285–1298. ACM (2017)
2017
Earlier work this paper cites.
Ye, K.: Anomaly detection in clouds: Challenges and practice. In: Proceedings of the First Workshop on Emerging Technologies for Software-Defined and Reconfigurable Hardware-Accelerated Cloud Datacenters. ETCD’17, Association for Computing Machinery (2017)
2017
Earlier work this paper cites.
Chen, A.R.: An empirical study on leveraging logs for debugging production failures. In: Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings (ICSE). p. 126–128. IEEE (2019)
2019
Earlier work this paper cites.
Yuan, Y., Shi, W., Liang, B., Qin, B.: An approach to cloud execution failure diagnosis based on exception logs in openstack. 2019 IEEE 12th International Conference on Cloud Computing (CLOUD) pp. 124–131 (2019)
2019
Earlier work this paper cites.
Zhang, X., Xu, Y., Lin, Q., Qiao, B., Zhang, H., Dang, Y., Xie, C., Yang, X., Cheng, Q., Li, Z., et al.: Robust log-based anomaly detection on unstable log data. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. pp. 807–817 (2019)
2019
Earlier work this paper cites.
Zhou, X., Peng, X., Xie, T., Sun, J., Ji, C., Liu, D., Xiang, Q., He, C.: Latent error prediction and fault localization for microservice applications by learning from system trace logs. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. pp. 683–694 (2019)
2019
Earlier work this paper cites.
Aggarwal, P., Gupta, A., Mohapatra, P., Nagar, S., Mandal, A., Wang, Q., Paradkar, A.: Localization of operational faults in cloud applications by mining causal dependencies in logs using golden signals (10 2020)
2020
Cited alongside, same era.
Ayed, F., Stella, L., Januschowski, T., Gasthaus, J.: Anomaly detection at scale: The case for deep distributional time series models (2020)
2020
Cited alongside, same era.
Beiran, C., Yi, Z., Isofidis, G.: Resource sharing in public cloud system with evolutionary multi-agent artificial swarm intelligence. In: AIOPS 2020-International Workshop on Artificial Intelligence for IT Operations (2020)
2020
Cited alongside, same era.
Bogatinovski, J., Nedelkoski, S., Cardoso, J., Kao, O.: Self-supervised anomaly detection from distributed traces. In: 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC). pp. 342–347. IEEE (2020)
2020
Cited alongside, same era.
Liu, X., Tong, Y., Xu, A., Akkiraju, R.: Using language models to pre-train features for optimizing information technology operations management tasks (10 2020)
2020
Later among the works it cites.
Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., Kao, O.: Self-attentive classification-based anomaly detection in unstructured logs (2020)
2020
Later among the works it cites.
Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., Kao, O.: Self-supervised log parsing (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Scheinert, D., Acker, A.: Telesto: A graph neural network model for anomaly classification in cloud services. In: 18th International Conference on Service-Oriented Computing. p. To appear. Springer (2020)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cotroneo, D., Simone, L.D., Liguori, P., Natella, R., Scibelli, A.: Towards runtime verification via event stream processing in cloud computing infrastructures (2020)
2020
Cited alongside, same era.
Fournier-Viger, P., Ganghuan, H., Zhou, M., Nouioua1, M., Liu, J.: Discovering alarm correlation rules for network fault management. In: AIOPS 2020-International Workshop on Artificial Intelligence for IT Operations (2020)
2020
Cited alongside, same era.
Jindal, A., Staab, P., Cardoso, J., Gerndt, M., Podolskiy, V.: Online memory leak detection in the cloud-based infrastructures (12 2020)
2020
Cited alongside, same era.
Keli, Z., Marcus, K., Min, Z., Xi, Z., Junjian, Y.: An influence-based approach for root cause alarm discovery in telecom networks. In: AIOPS 2020-International Workshop on Artificial Intelligence for IT Operations (2020)
2020
Cited alongside, same era.
Liu, P., Xu, H., Ouyang, Q., Jiao, R., Chen, Z., Zhang, S., Yang, J., Mo, L., Zeng, J., Xue, W., et al.: Unsupervised detection of microservice trace anomalies through service-level deep bayesian networks. In: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE). pp. 48–58. IEEE (2020)
2020
Cited alongside, same era.
2020
Later among the works it cites.
Shahid, A., White, G., Diuwe, J., Agapitos, A., O’brien, O.: Slmad: Statistical learning-based metric anomaly detection (12 2020)
2020
Later among the works it cites.
Wittkopp, T., Acker, A.: Decentralized federated learning preserves model and data privacy. In: 18th International Conference on Service-Oriented Computing. p. To appear. Springer (2020)
2020
Later among the works it cites.
Wu, L., Bogatinovski, J., Nedelkoski, S., Tordsson, J., Kao, O.: Performance diagnosis in cloud microservices using deep learning. In: AIOPS 2020-International Workshop on Artificial Intelligence for IT Operations (2020)
2020
Later among the works it cites.
Bogatinovski, J., Nedelkoski, S.: Multi-source anomaly detection in distributed it systems (2021)
2021
Closest in time.