{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T22:07:28Z","timestamp":1778969248922,"version":"3.51.4"},"reference-count":37,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:p>There is great potential in leveraging Artificial Intelligence (AI) systems to optimize complex infrastructures, automate difficult tasks, or support autonomy and coordination between networked devices. However, advances in state-of-theart AI often neglect features and\/or requirements that businesses care deeply about, namely traceability and explainability. A majority of available research has not explored much the deployment of semi-physical architectures combining fuzzy rulebased systems with more opaque models to improve explainability, being this specially true for the management of microservices in cloud and cloud-edge environments. This contribution builds on previous work that proposes a middle ground of mixed AI architectures that combine the performance of black-box AI models with \u201da more explainable overall architecture\u201d by implementing a microservice scaling system for distributed cloud environments using a cascade approach. This work demonstrates and evaluates an application case of such an approach departing from a Service Level Agreement compliance, in a case of microservice scaling decision over cloud (and cloud-like) infrastructures.<\/jats:p>","DOI":"10.2298\/csis250425014g","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T09:44:58Z","timestamp":1768988698000},"page":"561-583","source":"Crossref","is-referenced-by-count":0,"title":["Cascade systems as an implementation of a gray-box architecture: A case study in traceability for microservice scaling"],"prefix":"10.2298","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4855-7705","authenticated-orcid":false,"suffix":"Jim\u00e9nez","given":"Jorge","family":"Garc\u00eda","sequence":"first","affiliation":[{"name":"Universitat Polit\u00e8cnica de Valncia, Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6002-4050","authenticated-orcid":false,"suffix":"Lacalle","given":"Ignacio","family":"\u00dabeda","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia, Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0869-3836","authenticated-orcid":false,"given":"Pawe\u0142","family":"Szmeja","sequence":"additional","affiliation":[{"name":"Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3763-2373","authenticated-orcid":false,"given":"Katarzyna","family":"Wasielewska-Michniewska","sequence":"additional","affiliation":[{"name":"Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5425-7440","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Ho\u0142da","sequence":"additional","affiliation":[{"name":"Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-4844","authenticated-orcid":false,"given":"Maria","family":"Ganzha","sequence":"additional","affiliation":[{"name":"Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3795-5404","authenticated-orcid":false,"suffix":"E.","given":"Carlos","family":"Palau Salvador","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia, Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8480-9867","authenticated-orcid":false,"given":"Costin","family":"B\u0103dic\u0103","sequence":"additional","affiliation":[{"name":"Department of Computers and Information Technology, University of Craiova, Craiova, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8484-5849","authenticated-orcid":false,"given":"Stefka","family":"Fidanova","sequence":"additional","affiliation":[{"name":"Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8069-2152","authenticated-orcid":false,"given":"Marcin","family":"Paprzycki","sequence":"additional","affiliation":[{"name":"Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"W. Samek and K.-R. M\u00fcller, \u201dTowards explainable artificial intelligence,\u201d in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K.-R. M\u00fcller, Eds. Cham, Switzerland: Springer International Publishing, 2019, pp. 5-22. ISBN: 978-3-030-28954-6.","DOI":"10.1007\/978-3-030-28954-6_1"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"R. Dwivedi, D. Dave, H. Naik, et al., \u201cExplainable ai (xai): Core ideas, techniques, and solutions,\u201d ACM Comput. Surv., vol. 55, no. 9, Jan. 2023, ISSN: 0360- 0300. DOI: 10 . 1145 \/ 3561048. [Online]. Available: https:\/\/doi.org\/10.1145\/3561048","DOI":"10.1145\/3561048"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"A. Rai, \u201dExplainable AI: From black box to glass box,\u201d Journal of the Academy of Marketing Science, vol. 48, pp. 137-141, 2020","DOI":"10.1007\/s11747-019-00710-5"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"I. Ahmed, G. Jeon, and F. Piccialli, \u201dFrom artificial intelligence to explainable artificial intelligence in industry 4.0: A survey on what, how, and where,\u201d IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5031-5042, 2022. doi: 10.1109\/TII.2022.3146552.","DOI":"10.1109\/TII.2022.3146552"},{"key":"ref5","unstructured":"K. E. Mokhtari, B. P. Higdon, and A. Bas\uff0car, \u201dInterpreting financial time series with SHAP values,\u201d in Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, 2019, pp. 166-172."},{"key":"ref6","unstructured":"M. T. Ribeiro, S. Singh, and C. Guestrin, \u201dModel-agnostic interpretability of machine learning,\u201d arXiv preprint arXiv:1606.05386, 2016."},{"key":"ref7","unstructured":"E. Galinkin, \u201dRobustness and usefulness in AI explanation methods,\u201d arXiv preprint arXiv:2203.03729, 2022."},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"A. Gramegna and P. Giudici, \u201dSHAP and LIME: An evaluation of discriminative power in credit risk,\u201d Frontiers in Artificial Intelligence, vol. 4, p. 752-558, 2021.","DOI":"10.3389\/frai.2021.752558"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"V. Hassija, V. Chamola, A. Mahapatra, et al., \u201dInterpreting black-box models: A review on explainable artificial intelligence,\u201d Cognitive Computation, vol. 16, no. 1, pp. 45-74, 2024.","DOI":"10.1007\/s12559-023-10179-8"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"C. Rudin and J. Radin, \u201dWhy are we using black box models in AI when we don\u2019t need to? A lesson from an explainable AI competition,\u201d Harvard Data Science Review, vol. 1, no. 2, pp. 10-1162, 2019.","DOI":"10.1162\/99608f92.5a8a3a3d"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"C. Rudin, \u201dStop explaining black box machine learning models for high stakes decisions and use interpretable models instead,\u201d Nature Machine Intelligence, vol. 1, no. 5, pp. 206-215, 2019.","DOI":"10.1038\/s42256-019-0048-x"},{"key":"ref12","unstructured":"K. Arendt, M. Jradi, H. R. Shaker, and C. Veje, \u201dComparative analysis of white-, gray- and black-box models for thermal simulation of indoor environment: Teaching building case study,\u201d in Building Performance Analysis Conference and SimBuild: Co-organized by ASHRAE and IBPSA-USA, ASHRAE, 2018, pp. 173-180."},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"D. Thakker, B. K. Mishra, A. Abdullatif, S. Mazumdar, and S. Simpson, \u201dExplainable artificial intelligence for developing smart cities solutions,\u201d Smart Cities, vol. 3, no. 4, pp. 1353-1382, 2020, ISSN: 2624-6511. doi: 10.3390\/smartcities3040065. [Online]. Available: https:\/\/www.mdpi.com\/2624-6511\/3\/4\/65.","DOI":"10.3390\/smartcities3040065"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"M. Kunjir and S. Babu, \u201dBlack or white? How to develop an autotuner for memory-based analytics,\u201d in Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD\/PODS \u201920, ACM, May 2020. doi: 10.1145\/3318464.3380591. [Online]. Available: http:\/\/dx.doi.org\/10.1145\/3318464.3380591..","DOI":"10.1145\/3318464.3380591"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Y. Oussar and G. Dreyfus, \u201dHow to be a gray box: Dynamic semi-physical modeling,\u201d Neural Networks, vol. 14, no. 9, pp. 1161-1172, 2001.","DOI":"10.1016\/S0893-6080(01)00096-X"},{"key":"ref16","unstructured":"T. Gale, E. Elsen, and S. Hooker, \u201dThe state of sparsity in deep neural networks,\u201d arXiv preprint arXiv:1902.09574, 2019."},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"U. Forssell and P. Lindskog, \u201dCombining semi-physical and neural network modeling: An example of its usefulness,\u201d IFAC Proceedings Volumes, vol. 30, no. 11, pp. 767-770, 1997. doi: 10.1016\/S1474-6670(17)42938-7. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1474667017429387.","DOI":"10.1016\/S1474-6670(17)42938-7"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"J. Adou, A. Brou, and B. Porterie, \u201dModeling wildland fire propagation using a semi-physical network model,\u201d Case Studies in Fire Safety, vol. 4, pp. 11-18, 2015.","DOI":"10.1016\/j.csfs.2015.05.003"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"L. Haviez, R. Toscano, M. El Youssef, S. Fouvry, G. Yantio, and G. Moreau, \u201dSemi-physical neural network model for fretting wear estimation,\u201d Journal of Intelligent & Fuzzy Systems, vol. 28, no. 4, pp. 1745-1753, 2015.","DOI":"10.3233\/IFS-141461"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"L. A. Zadeh, \u201dFuzzy logic,\u201d Computer, vol. 21, no. 4, pp. 83-93, 1988.","DOI":"10.1109\/2.53"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"L. Magdalena, \u201dFuzzy rule-based systems,\u201d in Springer Handbook of Computational Intelligence, 2015, pp. 203-218.","DOI":"10.1007\/978-3-662-43505-2_13"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"P. Mishra, \u201dModel explainability for rule-based expert systems,\u201d in Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks, Springer, 2021, pp. 315-326.","DOI":"10.1007\/978-1-4842-7158-2_13"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"J. M. Mendel and P. P. Bonissone, \u201dCritical thinking about explainable AI (XAI) for rule-based fuzzy systems,\u201d IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3579-3593, 2021.","DOI":"10.1109\/TFUZZ.2021.3079503"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"L. S\u00e1nchez, I. Couso, and M. Gonz\u00e1lez, \u201dA design methodology for semi-physical fuzzy models applied to the dynamic characterization of LiFePO4 batteries,\u201d Applied Soft Computing, vol. 14, pp. 269-288, 2014. doi: 10.1016\/j.asoc.2013.03.020. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S156849461300135X.","DOI":"10.1016\/j.asoc.2013.03.020"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"I. Rodr\u00edguez-Fdez, M. Mucientes, and A. Bugar\u00edn, \u201dFruler: Fuzzy rule learning through evolution for regression,\u201d Information Sciences, vol. 354, pp. 1-18, 2016.","DOI":"10.1016\/j.ins.2016.03.012"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"L. Wang, Z. Mu, and H. Guo, \u201dFuzzy rule-based support vector regression system,\u201d Journal of Control Theory and Applications, vol. 3, no. 3, pp. 230-234, 2005.","DOI":"10.1007\/s11768-005-0040-3"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"R. Va\u00f1o, I. Lacalle, P. Sowi\u0144ski, R. S-Juli\u00e1n, and C. E. Palau, \u201dCloud-NativeWorkload Orchestration at the Edge: A Deployment Review and Future Directions,\u201d Sensors, vol. 23, no. 4, pp. 2215, 2023. doi: 10.3390\/s23042215.","DOI":"10.3390\/s23042215"},{"key":"ref28","unstructured":"Kubernetes, \u201cHorizontal Pod Autoscaler.\u201d Kubernetes, kubernetes.io\/docs\/tasks\/runapplication\/ horizontal-pod-autoscale\/."},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"F. -D. Eyitemi and S. Reiff-Marganiec, \u201dSystem Decomposition to Optimize Functionality Distribution in Microservices with Rule Based Approach,\u201d 2020 IEEE International Conference on Service Oriented Systems Engineering (SOSE), Oxford, UK, 2020, pp. 65-71, doi: 10.1109\/SOSE49046.2020.00015.","DOI":"10.1109\/SOSE49046.2020.00015"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"N. Liu, Z. Li, J. Xu, et al., \u201dA hierarchical framework of cloud resource allocation and power management using deep reinforcement learning,\u201d in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), IEEE, 2017, pp. 372-382.","DOI":"10.1109\/ICDCS.2017.123"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"E. Barrett, E. Howley, and J. Duggan, \u201dApplying reinforcement learning towards automating resource allocation and application scalability in the cloud,\u201d Concurrency and Computation: Practice and Experience, vol. 25, no. 12, pp. 1656-1674, 2013.","DOI":"10.1002\/cpe.2864"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Z. Ding, S. Wang and C. Jiang, \u201dKubernetes-Oriented Microservice Placement With Dynamic Resource Allocation,\u201d in IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1777- 1793, 1 April-June 2023, doi: 10.1109\/TCC.2022.3161900.","DOI":"10.1109\/TCC.2022.3161900"},{"key":"ref33","unstructured":"\u201daerOS project.\u201d https:\/\/aeros-project.eu (accessed May 25, 2024)."},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"R. S-Juli\u00e1n, I. Lacalle, R. Va\u00f1o, F. Boronat, and C. E. Palau, \u201dSelf-* Capabilities of Cloud-Edge Nodes: A Research Review,\u201d Sensors, vol. 23, no. 6, pp. 2931, 2023. doi: 10.3390\/s23062931.","DOI":"10.3390\/s23062931"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"X. He, H. Xu, X. Xu, Y. Chen and Z. Wang, \u201dAn Efficient Algorithm for Microservice Placement in Cloud-Edge Collaborative Computing Environment,\u201d in IEEE Transactions on Services Computing, doi: 10.1109\/TSC.2024.3399650.","DOI":"10.1109\/TSC.2024.3399650"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"P. Ngatchou, A. Zarei, and A. El-Sharkawi, \u201dPareto multi objective optimization,\u201d in Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, 2005, pp. 84-91. doi: 10.1109\/ISAP.2005.1599245.","DOI":"10.1109\/ISAP.2005.1599245"},{"key":"ref37","unstructured":"G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, \u201dImproving neural networks by preventing co-adaptation of feature detectors,\u201d arXiv preprint arXiv:1207.0580, 2012"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T21:35:59Z","timestamp":1778967359000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142600014G"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.2298\/csis250425014g","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}