FinOps–AIOps Fusion: Cost-Aware Anomaly Attribution for Microservice-Based Cloud Systems

Muhammad Jazib; Muhammad Haseeb Anees

Authors

Muhammad Jazib Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Muhammad Haseeb Anees Department of Computer Science, University of Central Punjab, Lahore, Pakistan

Keywords:

Distributed Tracing, Anomaly Detection, Cloud Native Systems, FinOps, AIOps

Abstract

Cloud-native microservice systems generate large volumes of operational telemetry and cloud billing data, making it increasingly difficult to balance system reliability and cost efficiency. Existing Artificial Intelligence for IT Operations (AIOps) systems focus on identifying technical anomalies, while Financial Operations (FinOps) systems provide cost visibility without linking costs to operational events. In this paper, a combined FinOps-AIOps hybrid model of cost-conscious anomaly attribution at the microservice level is proposed. The framework combines data from distributed tracing, system monitoring, and cloud billing in a fusion engine where the identified anomalies are correlated with their financial effect. The proposed system was tested within a simulated microservice environment based on Kubernetes with 20 services and a 7-day workload. Experimental results show that the framework achieves 92% anomaly detection accuracy with a precision of 0.90, a recall of 0.88, and an F1-score of 0.89. The model also attributes costs with approximately 85% accuracy, and cost-aware incident prioritization improves by 62% compared to AIOps-only models. The results show a statistically significant improvement in cost optimization efficiency without degrading detection performance (F1 deviation < 3%). The results suggest that integrating operational intelligence with financial analytics enables real-time, cost-sensitive decision-making, improving system reliability and cost optimization in cloud-based distributed environments.

References

D. D, P. K. C N, K. B. Sreenath, B. P. M L, and C. H. R. Varma, “DRL- Deep Reinforcement Learning Techniques for Dynamic Load Balancing in Cloud Infrastructure,” pp. 1–7, Nov. 2025, doi: 10.1109/ICCAMS65118.2025.11233935.

Sridhar Sampath, “AI-driven multi-cloud cost allocation: Transforming FinOps through automation,” World J. Adv. Eng. Technol. Sci., vol. 15, no. 2, pp. 203–210, 2025, doi: 10.30574/wjaets.2025.15.2.0290.

Saurabh Deochake, “ABACUS: A FinOps Service for Cloud Cost Optimization,” arXiv:2501.14753, 2024, [Online]. Available: https://arxiv.org/abs/2501.14753

Jacopo Soldani, Antonio Brogi, “Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Survey,” arXiv:2105.12378, 2021, [Online]. Available: https://arxiv.org/abs/2105.12378

Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip S. Yu, Ying Li, “A Survey of AIOps for Failure Management in the Era of Large Language Models,” arXiv:2406.11213, 2024, [Online]. Available: https://arxiv.org/abs/2406.11213

Shenglin Zhang, Sibo Xia, Wenzhao Fan, Binpeng Shi, Xiao Xiong, Zhenyu Zhong, Minghua Ma, Yongqian Sun, Dan Pei, “Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis,” arXiv:2407.01710, 2024, [Online]. Available: https://arxiv.org/abs/2407.01710

“(PDF) Financial Operations (FinOps) in Cloud: Integrating financial management practices in cloud operations.” Accessed: Mar. 24, 2026. [Online]. Available: https://www.researchgate.net/publication/383857569_Financial_Operations_FinOps_in_Cloud_Integrating_financial_management_practices_in_cloud_operations

S. Nedelkoski, J. Cardoso, and O. Kao, “Anomaly detection from system tracing data using multimodal deep learning,” IEEE Int. Conf. Cloud Comput. CLOUD, vol. 2019-July, pp. 179–186, Jul. 2019, doi: 10.1109/CLOUD.2019.00038.

Yu Gan, Mingyu Liang, “Sage: practical and scalable ML-driven performance debugging in microservices,” Int. Conf. Archit. Support Program. Lang. Oper. Syst. - ASPLOS, 2021, [Online]. Available: https://dl.acm.org/doi/10.1145/3445814.3446700

J. Wang, Y. Li, Q. Qi, Y. Lu, and B. Wu, “Multilayered Fault Detection and Localization With Transformer for Microservice Systems,” IEEE Trans. Reliab., vol. 73, no. 3, pp. 1502–1515, 2024, doi: 10.1109/TR.2024.3356717.

Iman Kohyarnejadfard, Daniel Aloise, Seyed Vahid Azhari & Michel R. Dagenais, “Anomaly detection in microservice environments using distributed tracing data analysis and NLP,” J. Cloud Comput., vol. 11, 2022, [Online]. Available: https://link.springer.com/article/10.1186/s13677-022-00296-4

Chenyu Zhao, Minghua Ma, Zhenyu Zhong, Shenglin Zhang, Zhiyuan Tan, Xiao Xiong, LuLu Yu, Jiayi Feng, Yongqian Sun, “Robust Multimodal Failure Detection for Microservice Systems,” arXiv:2305.18985, 2023, [Online]. Available: https://arxiv.org/abs/2305.18985

C. Zhang, “DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning,” 2022 IEEE/ACM 44th Int. Conf. Softw. Eng. (ICSE), Pittsburgh, PA, US, 2022, doi: 10.1145/3510003.3510180.

Ghaith Dkmak, Baris Can, “AI-Driven Anomaly Detection in Cloud-Native Microservices: The Night’s Watch Algorithm,” Appl. Sci., vol. 15, no. 23, p. 12762, 2025, doi: 10.3390/app152312762.

Ashwin Chavan, “Managing Scalability and Cost in Microservices ArchitectureBalancing Infinite Scalability with Financial Constraints,” J. Med. Healthc., 2023, doi: 10.47363/JMHC/2023(5)E102.

Preyashi Agarwal, J. Lakshmi, “Cost Aware Resource Sizing and Scaling of Microservices,” ACM Int. Conf. Proceeding Ser., 2019, [Online]. Available: https://dl.acm.org/doi/10.1145/3361821.3361823

M. Khanahmadi, A. Shameli-Sendi, M. Jabbarifar, Q. Fournier, and M. Dagenais, “Detection of microservice-based software anomalies based on OpenTracing in cloud,” Softw. - Pract. Exp., vol. 53, no. 8, pp. 1681–1699, Aug. 2023, doi: 10.1002/spe.3208.

L. Wu, J. Tordsson, E. Elmroth, and O. Kao, “MicroRCA: Root Cause Localization of Performance Issues in Microservices,” Proc. IEEE/IFIP Netw. Oper. Manag. Symp. 2020 Manag. Age Softwarization Artif. Intell. NOMS 2020, Apr. 2020, doi: 10.1109/NOMS47738.2020.9110353.

“Automated Root Cause Analysis Using Artificial Intelligence in Microservices-Oriented DevOps Frameworks | Request PDF.” Accessed: Mar. 24, 2026. [Online]. Available: https://www.researchgate.net/publication/394081700_Automated_Root_Cause_Analysis_Using_Artificial_Intelligence_in_Microservices-Oriented_DevOps_Frameworks

Ruyue Xin, Peng Chen, “CausalRCA: Causal inference based precise fine-grained root cause localization for microservice applications,” J. Syst. Softw., vol. 203, p. 111724, 2023, doi: https://doi.org/10.1016/j.jss.2023.111724.

Cheryl Lee, Tianyi Yang, Zhuangbin Chen, Yuxin Su, Michael R. Lyu, “Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data,” arXiv:2302.05092, 2023, [Online]. Available: https://arxiv.org/abs/2302.05092

“Integrated Real-Time Observability Framework for Transactional Microservices.” Accessed: Apr. 20, 2026. [Online]. Available: https://www.researchgate.net/publication/395232600_Integrated_Real-Time_Observability_Framework_for_Transactional_Microservices

Miguel De la Cruz Cabello, Tiago Prince Sales, “AIOps for log anomaly detection in the era of LLMs: A systematic literature review,” Intell. Syst. with Appl., vol. 28, p. 200608, 2025, doi: https://doi.org/10.1016/j.iswa.2025.200608.

Eduardo Teixeira Leite, “Cloud Cost Optimization: Strategies for Efficient Resource Management and Infrastructure Cost Reduction in Modern Cloud Environments,” Rev. Sist., vol. 14, no. 1, 2023, doi: 10.56238/rcsv14n1-003.

S. Mustafa, K. Bilal, S. U. R. Malik and S. A. Madani, “SLA-Aware Energy Efficient Resource Management for Cloud Environments,” IEEE Access, vol. 6, pp. 15004–15020, 2018, doi: 10.1109/ACCESS.2018.2808320.

Tariq Al-Omari, “Context-Aware Anomaly Detection in Microservices Using GCN‑Encoded Trace Graphs and LSTM‑AE Metrics with Local and Global Embeddings,” Eng. Technol. Appl. Sci. Res., vol. 15, no. 6, pp. 29277–29283, 2025, doi: 10.48084/etasr.13590.

Q. Li et al., “RAMBO: Resource Allocation for Microservices Using Bayesian Optimization,” IEEE Comput. Archit. Lett., vol. 20, no. 1, pp. 46–49, Jan. 2021, doi: 10.1109/LCA.2021.3066142.

“(PDF) Monitoring and Observability for Cloud-Native Applications.” Accessed: Apr. 20, 2026. [Online]. Available: https://www.researchgate.net/publication/399515451_Monitoring_and_Observability_for_Cloud-Native_Applications

Avinash Mysore Geethananda, “Finops In Multi-Cloud AI Environments: Financial Governance Strategies For Complex Computational Workloads,” J. Int. Cris. Risk Commun. Res., 2025, doi: 10.63278/jicrcr.vi.3460.