{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:01:23Z","timestamp":1770285683397,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brasil (CAPES)","award":["001"],"award-info":[{"award-number":["001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, the number of replicas used, and adherence to latency Service-Level Agreements (SLAs). The experiments were conducted in an environment simulating Edge Computing infrastructure, with virtual machines used to represent edge nodes and traffic generated via JMeter. The results demonstrate that FHS achieves a reduction in CPU consumption, uses fewer replicas under the same stress conditions, and exhibits more distributed SLA latency violation rates compared to HPA. These results indicate that FHS offers a more efficient and customizable solution for replica scaling in Kubernetes within Edge Computing environments, contributing to both operational efficiency and service quality.<\/jats:p>","DOI":"10.3390\/fi16090316","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Application of Fuzzy Logic for Horizontal Scaling in Kubernetes Environments within the Context of Edge Computing"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6958-6754","authenticated-orcid":false,"given":"S\u00e9rgio N.","family":"Silva","sequence":"first","affiliation":[{"name":"InovAI Lab, nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"},{"name":"Leading Advanced Technologies Center of Excellence (LANCE), nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2176-793X","authenticated-orcid":false,"given":"Mateus A. S. de S.","family":"Goldbarg","sequence":"additional","affiliation":[{"name":"InovAI Lab, nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"},{"name":"Leading Advanced Technologies Center of Excellence (LANCE), nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6282-9744","authenticated-orcid":false,"given":"Lucileide M. D. da","family":"Silva","sequence":"additional","affiliation":[{"name":"InovAI Lab, nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"},{"name":"Leading Advanced Technologies Center of Excellence (LANCE), nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"},{"name":"Federal Institute of Education, Science and Technology of Rio Grande do Norte, Paraiso, Santa Cruz 59200-000, RN, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-2506","authenticated-orcid":false,"given":"Marcelo A. C.","family":"Fernandes","sequence":"additional","affiliation":[{"name":"InovAI Lab, nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"},{"name":"Leading Advanced Technologies Center of Excellence (LANCE), nPITI\/IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, RN, Brazil"},{"name":"Department of Computer and Automation Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tran, M.N., Vu, D.D., and Kim, Y. (2022, January 5\u20138). A Survey of Autoscaling in Kubernetes. Proceedings of the 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), Barcelona, Spain.","DOI":"10.1109\/ICUFN55119.2022.9829572"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huo, Q., Li, S., Xie, Y., and Li, Z. (2022, January 21\u201323). Horizontal Pod Autoscaling based on Kubernetes with Fast Response and Slow Shrinkage. Proceedings of the 2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC), Kunming, China.","DOI":"10.1109\/AIIPCC57291.2022.00051"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102461","DOI":"10.1016\/j.simpat.2021.102461","article-title":"Reinforcement learning-assisted autoscaling mechanisms for serverless computing platforms","volume":"116","author":"Zafeiropoulos","year":"2022","journal-title":"Simul. Modell. Pract. Theory"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yao, A., Jiang, F., Li, X., Dong, C., Xu, J., Xu, Y., Li, G., and Liu, X. (2021, January 20\u201322). A novel security framework for edge computing based uav delivery system. Proceedings of the 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Shenyang, China.","DOI":"10.1109\/TrustCom53373.2021.00142"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Silva, S.N., da Silva, L.M., and Fernandes, M.A. (2023, January 21\u201324). L\u00f3gica Fuzzy Aplicada a Escalonamento Horizontal. Proceedings of the Anais Estendidos do XIII Simp\u00f3sio Brasileiro de Engenharia de Sistemas Computacionais, Porto Alegre, Brazil.","DOI":"10.5753\/sbesc_estendido.2023.235440"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100650","DOI":"10.1016\/j.cosrev.2024.100650","article-title":"Auto-scaling mechanisms in serverless computing: A comprehensive review","volume":"53","author":"Tari","year":"2024","journal-title":"Comput. Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.1007\/s10586-023-03999-8","article-title":"Intelligent microservices autoscaling module using reinforcement learning","volume":"26","author":"Ra","year":"2023","journal-title":"Clust. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khaleq, A.A., and Ra, I. (October, January 27). Development of QoS-aware agents with reinforcement learning for autoscaling of microservices on the cloud. Proceedings of the 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), Washington, DC, USA.","DOI":"10.1109\/ACSOS-C52956.2021.00025"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3527","DOI":"10.1109\/TNSM.2021.3066625","article-title":"AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach","volume":"18","author":"Sami","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xiao, Z., and Hu, S. (2022, January 28\u201330). DScaler: A Horizontal Autoscaler of Microservice Based on Deep Reinforcement Learning. Proceedings of the 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), Takamatsu, Japan.","DOI":"10.23919\/APNOMS56106.2022.9919994"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Santos, J., Wauters, T., Volckaert, B., and Turck, F.D. (2023, January 8\u201312). gym-hpa: Efficient Auto-Scaling via Reinforcement Learning for Complex Microservice-based Applications in Kubernetes. Proceedings of the NOMS 2023\u20142023 IEEE\/IFIP Network Operations and Management Symposium, Miami, FL, USA.","DOI":"10.1109\/NOMS56928.2023.10154298"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mondal, S.K., Wu, X., Kabir, H.M.D., Dai, H.N., Ni, K., Yuan, H., and Wang, T. (2023). Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes. Mathematics, 11.","DOI":"10.3390\/math11122675"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s10723-022-09634-x","article-title":"K-AGRUED: A Container Autoscaling Technique for Cloud-based Web Applications in Kubernetes Using Attention-based GRU Encoder-Decoder","volume":"20","author":"Dogani","year":"2022","journal-title":"J. Grid Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mudvari, A., Makris, N., and Tassiulas, L. (2021, January 7\u201311). ML-driven scaling of 5G Cloud-Native RANs. Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685874"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shim, S., Dhokariya, A., Doshi, D., Upadhye, S., Patwari, V., and Park, J.Y. (2023, January 7\u201320). Predictive Auto-scaler for Kubernetes Cloud. Proceedings of the 2023 IEEE International Systems Conference (SysCon), Vancouver, BC, Canada.","DOI":"10.1109\/SysCon53073.2023.10131106"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kakade, S., Abbigeri, G., Prabhu, O., Dalwayi, A., G, N., Patil, S.P., and Sunag, B. (2023, January 21\u201322). Proactive Horizontal Pod Autoscaling in Kubernetes using Bi-LSTM. Proceedings of the 2023 IEEE International Conference on Contemporary Computing and Communications (InC4), Bangalore, India.","DOI":"10.1109\/InC457730.2023.10263031"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1109\/TNSM.2021.3052837","article-title":"Machine Learning-Based Scaling Management for Kubernetes Edge Clusters","volume":"18","author":"Toka","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pahl, C., Vukovic, M., Yin, J., and Yu, Q. (2018). A Fuzzy-Based Auto-scaler for Web Applications in Cloud Computing Environments. Lecture Notes in Computer Science, Proceedings of the Service-Oriented Computing, Hangzhou, China, 12\u201315 November 2018, Springer.","DOI":"10.1007\/978-3-030-03596-9"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gand, F., Fronza, I., El Ioini, N., Barzegar, H.R., Azimi, S., and Pahl, C. (2020, January 7\u20139). A Fuzzy Controller for Self-adaptive Lightweight Edge Container Orchestration. Proceedings of the 10th International Conference on Cloud Computing and Services Science\u2014CLOSER, Prague, Czech Republic.","DOI":"10.5220\/0009379600790090"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.engappai.2019.08.010","article-title":"Heuristic design of fuzzy inference systems: A review of three decades of research","volume":"85","author":"Ojha","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_21","unstructured":"Rajakumar, G., Du, K.L., and Rocha, \u00c1. (2023). Fuzzy Logic and ANN in an Artificial Intelligent Cloud: A Comparative Study. Lecture Notes on Data Engineering and Communications Technologies, Proceedings of the Intelligent Communication Technologies and Virtual Mobile Networks, Tirunelveli, India, 16\u201317 February 2023, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Radwan, A.M., and Ellabib, I.M. (2023, January 21\u201323). Fuzzy Inference Systems for Load Balancing of Wireless Networks. Proceedings of the 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Benghazi, Libya.","DOI":"10.1109\/MI-STA57575.2023.10169569"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s10586-022-03550-1","article-title":"A high-efficiency learning model for virtual machine placement in mobile edge computing","volume":"25","author":"Jian","year":"2022","journal-title":"Clust. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102042","DOI":"10.1016\/j.rcim.2020.102042","article-title":"Development of an edge computing-based cyber-physical machine tool","volume":"67","author":"Zhang","year":"2021","journal-title":"Rob. Comput.-Integr. Manuf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alnoman, A. (September, January 30). Machine learning-based task clustering for enhanced virtual machine utilization in edge computing. Proceedings of the 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), London, ON, Canada.","DOI":"10.1109\/CCECE47787.2020.9255811"},{"key":"ref_26","unstructured":"(2023, July 18). MicroK8s: Lightweight Kubernetes. Available online: https:\/\/microk8s.io\/."},{"key":"ref_27","unstructured":"The Apache Software Foundation (2023, July 18). Apache JMeter. Available online: https:\/\/jmeter.apache.org\/."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Elkhatib, Y., and Poyato, J.P. (2023, January 23). An Evaluation of Service Mesh Frameworks for Edge Systems. Proceedings of the 6th International Workshop on Edge Systems, Analytics and Networking, Melbourne, Australia.","DOI":"10.1145\/3578354.3592867"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kabamba, H.M., Khouzam, M., and Dagenais, M.R. (2023). Vnode: Low-Overhead Transparent Tracing of Node. js-Based Microservice Architectures. Future Internet, 16.","DOI":"10.3390\/fi16010013"},{"key":"ref_30","first-page":"829","article-title":"Reference Method for Load Balancing in Web Services with REST Topology Using Edge Route Tools","volume":"Volume 4","author":"Sullon","year":"2021","journal-title":"Lecture Notes in Networks and Systems, Proceedings of the Sixth International Congress on Information and Communication Technology: ICICT 2021, London, UK, 25\u201326 February 2021"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/SERVICES.2019.00057","article-title":"An evaluation of open source serverless computing frameworks support at the edge","volume":"Volume 2642","author":"Palade","year":"2019","journal-title":"Proceedings of the 2019 IEEE World Congress on Services (SERVICES)"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lenka, R.K., Mamgain, S., Kumar, S., and Barik, R.K. (2018, January 12\u201313). Performance analysis of automated testing tools: JMeter and TestComplete. Proceedings of the 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India.","DOI":"10.1109\/ICACCCN.2018.8748521"},{"key":"ref_33","unstructured":"Dropwizard Development Team (2023, July 18). Dropwizard. Available online: https:\/\/www.dropwizard.io\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.vlsi.2022.09.005","article-title":"An energy-efficient single-cycle RV32I microprocessor for edge computing applications","volume":"88","author":"Shukla","year":"2023","journal-title":"Integration"},{"key":"ref_35","unstructured":"Hansson, G. (2021). Computation Offloading of 5G Devices at the Edge Using WebAssembly. [Master\u2019s Thesis, Lulea University of Technology]."},{"key":"ref_36","unstructured":"Red Hat (2023, July 18). Fabric8 Kubernetes-Client. Available online: https:\/\/github.com\/fabric8io\/kubernetes-client."},{"key":"ref_37","unstructured":"The Prometheus Authors (2023, July 18). Prometheus. Available online: https:\/\/prometheus.io\/."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101959","DOI":"10.1016\/j.jocs.2023.101959","article-title":"Online evaluation of the Kolmogorov-Smirnov test on arbitrarily large samples","volume":"67","author":"Cardoso","year":"2023","journal-title":"J. Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Stoker, P., Tian, G., and Kim, J.Y. (2020). Analysis of variance (ANOVA). Basic Quantitative Research Methods for Urban Planners, Routledge.","DOI":"10.4324\/9780429325021-11"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1080\/26939169.2021.2025177","article-title":"Alternate forms of the one-way ANOVA F and Kruskal-Wallis test statistics","volume":"30","author":"Johnson","year":"2022","journal-title":"J. Stat. Data Sci. Educ."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/9\/316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:46:56Z","timestamp":1760111216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/9\/316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,2]]},"references-count":40,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["fi16090316"],"URL":"https:\/\/doi.org\/10.3390\/fi16090316","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,2]]}}}