參考文獻: | [1] S. Sekigawa, C. Sasaki and A. Tagami, "Toward a Cloud-Native Telecom Infrastructure: Analysis and Evaluations of Kubernetes Networking," 2022 IEEE Globecom Workshops (GC Wkshps), Rio de Janeiro, Brazil, 2022, pp. 838-843, doi: 10.1109/GCWkshps56602.2022.10008579. [2] C. Liu, Z. Cai, B. Wang, Z. Tang and J. Liu, "A protocol-independent container network observability analysis system based on eBPF," 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), Hong Kong, 2020, pp. 697-702, doi: 10.1109/ICPADS51040.2020.00099. [3] C. Wen-Lin, "Intelligent Analysis for Abnormal Signaling Detection on NGN Voice Call," 2022 7th International Conference on Information and Network Technologies (ICINT), Okinawa, Japan, 2022, pp. 10-15, doi: 10.1109/ICINT55083.2022.00009. [4] D. Gedia and L. Perigo, "Decision-Tree Placement Algorithm for Containerized VoIP VNFs: A Network Management Approach," 2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN), Paris, France, 2022, pp. 1-5, doi: 10.1109/ICIN53892.2022.9758119. [5] W. -C. Chung, H. -T. Liang and Y. -H. Wang, "Performance Impacts of Scaling Policies for virtual IP Multimedia Subsystem on the Cloud," 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Phoenix, AZ, USA, 2022, pp. 13-18, doi: 10.1109/NFV-SDN56302.2022.9974953. [6] R. Chowdhury, C. Talhi, H. Ould-Slimane and A. Mourad, "Proactive and Intelligent Monitoring and Orchestration of Cloud-Native IP Multimedia Subsystem," in IEEE Open Journal of the Communications Society, vol. 5, pp. 139-155, 2024, doi: 10.1109/OJCOMS.2023.3341002. keywords: {Cloud computing;Monitoring;Microservice [7] F. Cecchinato, L. Vangelista, G. Biondo and M. Franchin, "Anomaly detection using LSTM neural networks: an application to VoIP traffic," 2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Shanghai, China, 2021, pp. 1-7, doi: 10.1109/RASSE53195.2021.9686840. [8] A. Diamanti, J. M. S. Vílchez and S. Secci, "An AI-Empowered Framework for Cross-Layer Softwarized Infrastructure State Assessment," in IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4434-4448, Dec. 2022, doi: 10.1109/TNSM.2022.3161872 [9] H. Zhang, Y. Xia, T. Yan and G. Liu, "Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder," 2021 33rd Chinese Control and Decision Conference (CCDC), Kunming, China, 2021, pp. 281-286, doi: 10.1109/CCDC52312.2021.9601669. [10] J. Kang, M. Kim, J. Park and S. Park, "Time-Series to Image-Transformed Adversarial Autoencoder for Anomaly Detection," in IEEE Access, vol. 12, pp. 119671-119684, 2024, doi: 10.1109/ACCESS.2024.3450709. [11] Y. Fang, J. Xie, Y. Zhao, L. Chen, Y. Gao and K. Zheng, "Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection," 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 2024, pp. 1228-1241, doi: 10.1109/ICDE60146.2024.00099. [12] K. Aykurt, A. Blenk and W. Kellerer, "NetLLMBench: A Benchmark Framework for Large Language Models in Network Configuration Tasks," 2024 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Natal, Brazil, 2024, pp. 1-6, doi: 10.1109/NFV-SDN61811.2024.10807499. [13] B. Bokkena, "Optimizing Cloud Infrastructure Management Using Large Language Models: A DevOps Perspective," 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2024, pp. 1401-1406, doi: 10.1109/ICSSAS64001.2024.10760725. [14] Bahaa, A.; Shehata, M.; Gasser, S.M.; El-Mahallawy, M.S. Call Failure Prediction in IP Multimedia Subsystem (IMS) Networks. Appl. Sci. 2022, 12, 8378. https://doi.org/10.3390/app12168378 [15] M. D. Mauro, G. Galatro, F. Postiglione and M. Tambasco, "Performability of Network Service Chains: Stochastic Modeling and Assessment of Softwarized IP Multimedia Subsystem," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 5, pp. 3071-3086, 1 Sept.-Oct. 2022, doi: 10.1109/TDSC.2021.3082626. [16] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. arXiv. https://arxiv.org/abs/1706.03762 [17] E. Schooler, J. Rosenberg, H. Schulzrinne, A. Johnston, G. Camarillo, J. Peterson, R. Sparks, and M. J. Handley, “RFC 3261: SIP: Session Initiation Protocol,” IETF, 2002. [Online]. Available: https://datatracker.ietf.org/doc/html/rfc3261 [18] Prometheus, "Configuration," Prometheus Documentation. [Online]. Available: https://prometheus.io/docs/prometheus/latest/configuration/configuration/ [19] Google, “Prompt engineering techniques,” Google Developers, [Online]. Available: https://developers.google.com/machine-learning/resources/prompt-eng?hl=zh-tw [20] Kamailio, “Kamailio v5.5.x Module Documentation,” [Online]. Available: https://kamailio.org/docs/modules/5.5.x/ [21] W. Attaoui, E. Sabir, H. Elbiaze and M. Guizani, "VNF and CNF Placement in 5G: Recent Advances and Future Trends," in IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4698-4733, Dec. 2023, doi: 10.1109/TNSM.2023.3264005. [22] A. I. Kouachi and I. E. Mokrane, "Leveraging Large Language Models for Base Station in Telecommunications," 2024 1st International Conference on Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), Oum El Bouaghi, Algeria, 2024, pp. 1-8, doi: 10.1109/ECTE-Tech62477.2024.10851026. [23] A. Dubey, C. P. Singh and D. Nadig, "Leveraging Large Language Models for Intent-Based Generation of Cloud-Native Configurations," 2024 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Guwahati, India, 2024, pp. 1-6, doi: 10.1109/ANTS63515.2024.10898244. [24] V. -H. Le and H. Zhang, "Log Parsing: How Far Can ChatGPT Go?," 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), Luxembourg, Luxembourg, 2023, pp. 1699-1704, doi: 10.1109/ASE56229.2023.00206. [25] K. Panchal et al., "A Study on the Use of Runtime Files in Handling Crash Reports in a Large Telecom Company," 2022 IEEE Future Networks World Forum (FNWF), Montreal, QC, Canada, 2022, pp. 98-103, doi: 10.1109/FNWF55208.2022.00026. [26] Y. Kim and P. Thulasiraman, "Anomaly Detection in 5G Networks Using Transformer-Based Autoencoder," 2024 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Taichung, Taiwan, 2024, pp. 1-8, doi: 10.1109/RASSE64357.2024.10773774. [27] K. Zhang, G. Lu and Y. Li, "Trans-DAE: Transformer-based Double Autoencoder for Anomaly Detection on Attributed Networks," 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Harbin, China, 2023, pp. 1-9, doi: 10.1109/ICNC-FSKD59587.2023.10280911. [28] A. Hilt, J. Zemán and G. Járó, "Dimensioning Challenges of Telecommunication Network Elements Migrating Onto the Cloud (Invited Paper)," 2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), Graz, Austria, 2024, pp. 1-10, doi: 10.1109/CoBCom62281.2024.10631212. [29] X. Chen, S. Wang, S. -a. Wang, B. Ding, R. Yan and X. Chen, "Algorithm Unrolling Network With Learnable Sparse Regularization for Interpretable Mechanical Anomaly Detection," in IEEE Transactions on Industrial Informatics, vol. 21, no. 5, pp. 3786-3795, May 2025, doi: 10.1109/TII.2025.3528557. [30] Z. Yang, Y. Jin, J. Liu, X. Xu, Y. Zhang and S. Ji, "Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models," 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE), Guangzhou, China, 2025, pp. 1029-1032, doi: 10.1109/CISCE65916.2025.11065413. [31] Alvares C, Dinesh D, Alvi S, et al. Dataset of attacks on a live enterprise VoIP network for machine learning based intrusion detection and prevention systems[J]. Computer Networks, 2021 : 108283. |