{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:17:47Z","timestamp":1770877067616,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Service recommendation aims to assist users in selecting appropriate services according to their requirements while ensuring seamless compatibility in modern cloud and edge computing environments. In dynamic multi-cloud scenarios, services are typically deployed across heterogeneous cloud platforms and are frequently reconfigured. However, most existing service recommendation approaches primarily focus on static compatibility aspects, such as service interfaces or communication protocols, while overlooking the dynamic characteristics of service interactions. However, several limitations can be identified. First, the lack of effective mechanisms for quantifying service compatibility in dynamic cloud environments often leads to degraded system efficiency. Second, the absence of dedicated multi-cloud service compatibility quantification methodologies restricts recommendation accuracy. Third, insufficient mathematical analysis with respect to uniqueness, feasibility, and correctness may result in unstable evaluation outcomes and additional computational overhead. To overcome these limitations, this paper presents McCom, a multi-cloud service recommendation framework designed to quantify service compatibility performance and address the aforementioned challenges. First, a novel Markov chain-based compatibility quantification model is developed to characterize service interactions in dynamic multi-cloud environments. By exploiting the homogeneity, irreducibility, and convergence properties of Markov chains, the proposed model enables stable and reliable compatibility assessment. Second, a multi-cloud compatibility quantification strategy is introduced to mitigate interference arising from complex service pools through refined filtering and sketching mechanisms. Third, a series of mathematical proofs are provided to rigorously demonstrate the feasibility, correctness, and uniqueness of the proposed quantification method. Extensive simulation results indicate that the proposed framework achieves significant performance improvements, including enhancements in recommendation quality (14.44% in F1 score), reductions in latency (40.68%), and increases in accuracy (50.85%), compared with existing state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/computers15020085","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T10:48:01Z","timestamp":1770115681000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Addressing Compatibility Challenges in Multi-Cloud Services: A Markov Chain-Based Service Recommendation Framework"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6140-5496","authenticated-orcid":false,"given":"Shiyang","family":"Ma","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingtao","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojie","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zesong","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuewen","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/TSC.2022.3166553","article-title":"Serverless Computing: State-of-the-Art, Challenges and Opportunities","volume":"16","author":"Li","year":"2023","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ficili, I., Giacobbe, M., Tricomi, G., and Puliafito, A. (2025). From sensors to data intelligence: Leveraging IoT, cloud, and edge computing with AI. Sensors, 25.","DOI":"10.3390\/s25061763"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Panagou, I.C., Katsoulis, S., Nannos, E., Zantalis, F., and Koulouras, G. (2025). A comprehensive evaluation of IoT cloud platforms: A feature-driven review with a decision-making tool. Sensors, 25.","DOI":"10.3390\/s25165124"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Roumeliotis, A.J., Myritzis, E., Kosmatos, E., Katsaros, K.V., and Amditis, A.J. (2025). Multi-Area, Multi-Service and Multi-Tier Edge-Cloud Continuum Planning. Sensors, 25.","DOI":"10.3390\/s25133949"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103476","DOI":"10.1016\/j.jnca.2022.103476","article-title":"RPL routing protocol over IoT: A comprehensive survey, recent advances, insights, bibliometric analysis, recommendations, and future directions","volume":"207","author":"Darabkh","year":"2022","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_6","first-page":"2525","article-title":"A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction","volume":"34","author":"Wu","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TSC.2020.2980793","article-title":"A Survey on Web Service QoS Prediction Methods","volume":"15","author":"Ghafouri","year":"2022","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5225","DOI":"10.1109\/TKDE.2021.3059506","article-title":"Context-Aware Service Recommendation Based on Knowledge Graph Embedding","volume":"34","author":"Mezni","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TSC.2019.2944596","article-title":"Context-Aware and Adaptive QoS Prediction for Mobile Edge Computing Services","volume":"15","author":"Liu","year":"2022","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1109\/TCSS.2021.3064213","article-title":"Robust Collaborative Filtering Recommendation with User-Item-Trust Records","volume":"9","author":"Wang","year":"2022","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/TETCI.2020.3023155","article-title":"Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective","volume":"6","author":"Gao","year":"2022","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_12","first-page":"1","article-title":"Cloud-based XR services: A survey on relevant challenges and enabling technologies","volume":"2","author":"Theodoropoulos","year":"2022","journal-title":"J. Netw. Netw. Appl."},{"key":"ref_13","first-page":"78","article-title":"Dynamic SIoT Network Status Prediction","volume":"2","author":"Hu","year":"2022","journal-title":"J. Netw. Netw. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.S. (2017). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee.","DOI":"10.1145\/3038912.3052569"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rendle, S. (2010). Factorization machines. Proceedings of the 2010 IEEE International Conference on Data Mining, IEEE.","DOI":"10.1109\/ICDM.2010.127"},{"key":"ref_16","first-page":"1045","article-title":"Efficient QoS-aware service recommendation for multi-tenant service-based systems in cloud","volume":"13","author":"Wang","year":"2017","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"35645","DOI":"10.1109\/ACCESS.2019.2902131","article-title":"Quality-aware service selection for multi-tenant service oriented systems based on combinatorial auction","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","first-page":"6575","article-title":"A survey of context-aware recommender systems: From an evaluation perspective","volume":"35","author":"Meng","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Q., Ge, Y., Li, Z., Chen, E., and Xiong, H. (2011). Personalized travel package recommendation. Proceedings of the 2011 IEEE 11th International Conference on Data Mining, IEEE.","DOI":"10.1109\/ICDM.2011.118"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lara-Cabrera, R., Gonz\u00e1lez-Prieto, \u00c1., and Ortega, F. (2020). Deep matrix factorization approach for collaborative filtering recommender systems. Appl. Sci., 10.","DOI":"10.3390\/app10144926"},{"key":"ref_21","unstructured":"Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., and Achan, K. (2021). Rethinking neural vs. matrix-factorization collaborative filtering: The theoretical perspectives. Proceedings of the International Conference on Machine Learning, Virtual, 18\u201324 July 2021, PMLR."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Furnadzhiev, R., Shopov, M., and Kakanakov, N. (2025). Efficient Orchestration of Distributed Workloads in Multi-Region Kubernetes Cluster. Computers, 14.","DOI":"10.3390\/computers14040114"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Daki\u0107, V., Red\u017eepagi\u0107, J., Ba\u0161i\u0107, M., and \u017dgrabli\u0107, L. (2024). Methodology for automating and orchestrating performance evaluation of Kubernetes container network interfaces. Computers, 13.","DOI":"10.3390\/computers13110283"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nascimento, B., Santos, R., Henriques, J., Bernardo, M.V., and Caldeira, F. (2024). Availability, scalability, and security in the migration from container-based to cloud-native applications. Computers, 13.","DOI":"10.3390\/computers13080192"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TCSS.2020.2965234","article-title":"Location-aware service recommendations with privacy-preservation in the Internet of Things","volume":"8","author":"Lin","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TSC.2014.2365797","article-title":"Trust management for SOA-based IoT and its application to service composition","volume":"9","author":"Chen","year":"2014","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TSC.2010.52","article-title":"QoS-aware web service recommendation by collaborative filtering","volume":"4","author":"Zheng","year":"2010","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8075","DOI":"10.1016\/j.eswa.2014.07.012","article-title":"Social network-based service recommendation with trust enhancement","volume":"41","author":"Deng","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/JIOT.2020.3016659","article-title":"A social-relationships-based service recommendation system for SIoT devices","volume":"8","author":"Khelloufi","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Casino, F., Patsakis, C., Batista, E., Postolache, O., Mart\u00ednez-Ballest\u00e9, A., and Solanas, A. (2018). Smart healthcare in the IoT era: A context-aware recommendation example. Proceedings of the 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI), IEEE.","DOI":"10.1109\/ISSI.2018.8538106"},{"key":"ref_31","unstructured":"Erdeniz, S.P., Maglogiannis, I., Menychtas, A., Felfernig, A., and Tran, T.N.T. (2018). Recommender systems for IoT enabled m-health applications. Proceedings of the Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25\u201327, 2018, Proceedings 14, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100083","DOI":"10.1016\/j.smhl.2019.100083","article-title":"IAMHAPPY: Towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness","volume":"15","author":"Gyrard","year":"2020","journal-title":"Smart Health"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1109\/TSC.2024.3463474","article-title":"Distributed Computing Applications of Context-Aware QoS and Trust Prediction Framework","volume":"17","author":"Gamage","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1007\/s11036-017-0929-3","article-title":"emHealth: Towards emotion health through depression prediction and intelligent health recommender system","volume":"23","author":"Yang","year":"2018","journal-title":"Mob. Netw. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7038259","DOI":"10.1155\/2019\/7038259","article-title":"A travel route recommendation system based on smart phones and iot environment","volume":"2019","author":"Bin","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_36","unstructured":"Khani, M., Wang, Y., Orgun, M.A., and Zhu, F. (2018). Context-aware trustworthy service evaluation in social internet of things. Proceedings of the Service-Oriented Computing: 16th International Conference, ICSOC 2018, Hangzhou, China, 12\u201315 November 2018, Proceedings 16, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, T., Chen, T., Sun, Y., and Xu, Y. (2025). IM-GNN: Microservice Orchestration Recommendation via Interface-Matched Dependency Graphs and Graph Neural Networks. Symmetry, 17.","DOI":"10.3390\/sym17040525"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.32604\/iasc.2023.030484","article-title":"QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment","volume":"37","author":"Ma","year":"2023","journal-title":"Intell. Autom. Soft Comput."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/2\/85\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T05:25:00Z","timestamp":1770873900000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/2\/85"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,1]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["computers15020085"],"URL":"https:\/\/doi.org\/10.3390\/computers15020085","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,1]]}}}