{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:52:35Z","timestamp":1784199155663,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chengdu Key Research and Development Support Program \u201cJie Bang Gua Shuai\u201d Project","award":["2023-JB00-00012-GX"],"award-info":[{"award-number":["2023-JB00-00012-GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Microservice workflow orchestration recommendation aims to streamline business process construction by suggesting relevant microservices, yet existing methods relying on functional similarity in dependency graphs prove inadequate. Traditional graphs cluster functionally analogous microservices, neglecting execution-order dependencies critical for orchestration. This paper introduces a novel interface-matching-based approach to construct microservice dependency graphs, addressing the incompatibility of current methods with orchestration scenarios. The proposed method leverages a TF-WF-IDF algorithm and language models to extract input\u2013output representations from microservice documentation, followed by interface-matching algorithms to establish call dependencies. By capturing the inherent structural symmetry in microservice interactions, where balanced and reciprocal relationships between inputs and outputs guide service connectivity, our approach enhances the fidelity of dependency graphs. Building on this graph, we present IM-GNN, a graph neural network-based recommendation model that generates microservice embeddings and computes node similarities to recommend orchestration candidates. Experiments on Amazon\u2019s SageMaker and Comprehend datasets validate the model\u2019s effectiveness, demonstrating superior recommendation accuracy compared to traditional methods. Key contributions include the interface-driven graph construction framework, the IM-GNN model, and empirical insights into hyperparameter impacts. This work bridges the gap between dependency graph quality and orchestration needs, offering a foundation for integrating deep learning with microservice workflow design while highlighting the role of symmetry in structuring service dependencies and optimizing orchestration patterns.<\/jats:p>","DOI":"10.3390\/sym17040525","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T05:21:04Z","timestamp":1743398464000},"page":"525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["IM-GNN: Microservice Orchestration Recommendation via Interface-Matched Dependency Graphs and Graph Neural Networks"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-8884","authenticated-orcid":false,"given":"Taiyin","family":"Zhao","sequence":"first","affiliation":[{"name":"Laboratory of Intelligent Collaborative Computing, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Chen","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Collaborative Computing, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudong","family":"Sun","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Collaborative Computing, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0005-9201","authenticated-orcid":false,"given":"Yi","family":"Xu","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Collaborative Computing, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ko, H., Lee, S., Park, Y., and Choi, A. (2022). A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. Electronics, 11.","DOI":"10.3390\/electronics11010141"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Z., Shen, X., Jiao, Y., Pan, X., Zou, P., Meng, X., Yao, C., and Bu, J. (2020, January 20\u201324). Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA.","DOI":"10.1109\/ICDE48307.2020.00149"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"McAuley, J. (2022). Personalized Machine Learning, Cambridge University Press.","DOI":"10.1017\/9781009003971"},{"key":"ref_4","unstructured":"Qian, F., Pan, S., and Zhang, G. (2025). Tensor Computation for Seismic Data Processing: Linking Theory and Practice, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6889","DOI":"10.1109\/TKDE.2024.3392335","article-title":"Recommender Systems in the Era of Large Language Models (LLMs)","volume":"36","author":"Fan","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Saboor, A., Hassan, M.F., Akbar, R., Shah, S.N.M., Hassan, F., Magsi, S.A., and Siddiqui, M.A. (2022). Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives. Appl. Sci., 12.","DOI":"10.3390\/app12125793"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3901","DOI":"10.1109\/TPDS.2022.3174631","article-title":"An In-Depth Study of Microservice Call Graph and Runtime Performance","volume":"33","author":"Luo","year":"2022","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Su, Y., Li, Y., and Zhang, Z. (2025). Two-Tower Structure Recommendation Method Fusing Multi-Source Data. Electronics, 14.","DOI":"10.3390\/electronics14051003"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MCI.2024.3363984","article-title":"Recent Developments in Recommender Systems: A Survey [Review Article]","volume":"19","author":"Li","year":"2024","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huang, S., Wang, C., and Bian, W. (2024). A Hybrid Food Recommendation System Based on MOEA\/D Focusing on the Problem of Food Nutritional Balance and Symmetry. Symmetry, 16.","DOI":"10.3390\/sym16121698"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vaidhyanathan, K., Caporuscio, M., Florio, S., and Muccini, H. (2024, January 8\u201312). ML-enabled Service Discovery for Microservice Architecture: A QoS Approach. Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing, New York, NY, USA. SAC \u201924.","DOI":"10.1145\/3605098.3635942"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dang, Q., Li, N., Dong, H., Li, X., and Guo, M. (2024, January 20\u201322). Improved Microservice Fault Prediction Model of Informer Network. Proceedings of the 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.","DOI":"10.1109\/ITNEC60942.2024.10733306"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ampatzoglou, A., P\u00e9rez, J., Buhnova, B., Lenarduzzi, V., Venters, C.C., Zdun, U., Drira, K., Rebelo, L., Di Pompeo, D., and Tucci, M. (2024). Improving QoS of Microservices Architecture Using Machine Learning Techniques. Proceedings of the Software Architecture. ECSA 2024 Tracks and Workshops, Springer.","DOI":"10.1007\/978-3-031-71246-3"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Niu, B., Ma, J., and Yang, Z. (2021, January 19\u201321). A Comparative Study of CF and NCF in Children\u2019s Book Recommender System. Proceedings of the 2021 3rd International Workshop on Artificial Intelligence and Education (WAIE), Xi\u2019an, China.","DOI":"10.1109\/WAIE54146.2021.00017"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mao, C., Wu, Z., Liu, Y., and Shi, Z. (2024). Matrix Factorization Recommendation Algorithm Based on Attention Interaction. Symmetry, 16.","DOI":"10.3390\/sym16030267"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rendle, S. (2011, January 20). Factorization Machines. Proceedings of the 2010 IEEE International Conference on Data Mining, Sydney, NSW, Australia.","DOI":"10.1109\/ICDM.2010.127"},{"key":"ref_17","first-page":"1","article-title":"Improved Low-Rank Tensor Approximation for Seismic Random Plus Footprint Noise Suppression","volume":"61","author":"Qian","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Juan, Y., Zhuang, Y., Chin, W.S., and Lin, C.J. (2016, January 15\u201319). Field-aware Factorization Machines for CTR Prediction. Proceedings of the 10th ACM Conference on Recommender Systems, New York, NY, USA. RecSys \u201916.","DOI":"10.1145\/2959100.2959134"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., and He, X. (2017). DeepFM: A factorization-machine based neural network for CTR prediction. arXiv.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alhwayzee, A., Araban, S., and Zabihzadeh, D. (2025). A Robust Recommender System Against Adversarial and Shilling Attacks Using Diffusion Networks and Self-Adaptive Learning. Symmetry, 17.","DOI":"10.3390\/sym17020233"},{"key":"ref_21","first-page":"1","article-title":"Ground Truth-Free 3-D Seismic Random Noise Attenuation via Deep Tensor Convolutional Neural Networks in the Time-Frequency Domain","volume":"60","author":"Qian","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2010). TF\u2013IDF. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_23","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_24","unstructured":"Wittig, A. (2023). Amazon Web Services in Action, Simon and Schuster. [3rd ed.]."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rashed, A., Grabocka, J., and Schmidt-Thieme, L. (2021, January 11\u201315). A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA. SIGIR \u201921.","DOI":"10.1145\/3404835.3462864"},{"key":"ref_26","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kang, W.C., and McAuley, J. (2018, January 17\u201320). Self-Attentive Sequential Recommendation. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), Los Alamitos, CA, USA.","DOI":"10.1109\/ICDM.2018.00035"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, C., Ma, L., Zhang, Y., Sun, J., Liu, X., and Coates, M. (2020, January 7\u201312). Memory augmented graph neural networks for sequential recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.5945"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/525\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:06:25Z","timestamp":1760029585000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,31]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040525"],"URL":"https:\/\/doi.org\/10.3390\/sym17040525","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,31]]}}}