{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:58:23Z","timestamp":1780595903560,"version":"3.54.1"},"reference-count":66,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006013","name":"United Arab Emirates University (UAEU)","doi-asserted-by":"publisher","award":["12T047"],"award-info":[{"award-number":["12T047"]}],"id":[{"id":"10.13039\/501100006013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions.<\/jats:p>","DOI":"10.3390\/s24175551","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T03:57:06Z","timestamp":1724817426000},"page":"5551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions"],"prefix":"10.3390","volume":"24","author":[{"given":"Saleha","family":"Alharthi","sequence":"first","affiliation":[{"name":"Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afra","family":"Alshamsi","sequence":"additional","affiliation":[{"name":"Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anoud","family":"Alseiari","sequence":"additional","affiliation":[{"name":"Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulmalik","family":"Alwarafy","sequence":"additional","affiliation":[{"name":"Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Goel, P.K., Gulati, S., Singh, A., Tyagi, A., Komal, K., and Mahur, L.S. (2024, January 15\u201316). Energy-Efficient Block-Chain Solutions for Edge and Cloud Computing Infrastructures. Proceedings of the 2024 2nd International Conference on Disruptive Technologies (ICDT), Greater Noida, India.","DOI":"10.1109\/ICDT61202.2024.10489584"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ismahene, N.W., Souheila, B., and Nacereddine, Z. (2020, January 28\u201330). An Auto Scaling Energy Efficient Approach in Apache Hadoop. Proceedings of the 2020 International Conference on Advanced Aspects of Software Engineering (ICAASE), Constantine, Algeria.","DOI":"10.1109\/ICAASE51408.2020.9380109"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7329","DOI":"10.1109\/ACCESS.2023.3339250","article-title":"E-learning based Cloud Computing Environment: A Systematic Review, Challenges, and Opportunities","volume":"12","author":"Eljak","year":"2023","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"169","DOI":"10.37934\/araset.44.1.169180","article-title":"Risk Classes of Cloud Computing Project in Healthcare: A Review of Technical Report and Standards","volume":"44","author":"Fathullah","year":"2024","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bodemer, O. (2024). Revolutionizing Finance: The Impact of AI and Cloud Computing in the Banking Sector. TechRxiv.","DOI":"10.36227\/techrxiv.170974067.74825398\/v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1016\/j.procs.2024.01.139","article-title":"Cloud Usage for Manufacturing: Challenges and Opportunities","volume":"232","author":"Kiatipis","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sao Cao, D., Nguyen, D.T., Nguyen, X.C., Nguyen, H.B., Lang, K.T., Dao, N.L., Pham, T.T., Cao, N.S., Chu, D.H., and Nguyen, P.H. (2023, January 19\u201322). Elastic auto-scaling architecture in telco cloud. Proceedings of the 2023 25th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, Republic of Korea.","DOI":"10.23919\/ICACT56868.2023.10079575"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1504\/IJCC.2024.136286","article-title":"A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures","volume":"13","author":"Boujelben","year":"2024","journal-title":"Int. J. Cloud Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Singh, P.D., and Singh, K.D. (2024). Interdisciplinary Approaches: Fog\/Cloud Computing and IoT for AI and Robotics Integration. EAI Endorsed Trans. AI Robot., 3.","DOI":"10.4108\/airo.3620"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1109\/TSE.2019.2934849","article-title":"A cost-efficient auto-scaling algorithm for large-scale graph processing in cloud environments with heterogeneous resources","volume":"47","author":"Heidari","year":"2019","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1016\/j.future.2019.07.042","article-title":"Optimizing the performance of optimization in the cloud environment\u2013An intelligent auto-scaling approach","volume":"101","author":"Simic","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fourati, M.H., Marzouk, S., and Jmaiel, M. (2023, January 9\u201312). Towards Microservices-Aware Autoscaling: A Review. Proceedings of the 2023 IEEE Symposium on Computers and Communications (ISCC), Gammarth, Tunisia.","DOI":"10.1109\/ISCC58397.2023.10218213"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"399","DOI":"10.12694\/scpe.v20i2.1537","article-title":"Research on auto-scaling of web applications in cloud: Survey, trends and future directions","volume":"20","author":"Singh","year":"2019","journal-title":"Scalable Comput. Pract. Exp."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3148149","article-title":"Auto-scaling web applications in clouds: A taxonomy and survey","volume":"51","author":"Qu","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104288","DOI":"10.1016\/j.engappai.2021.104288","article-title":"Reinforcement learning-based application autoscaling in the cloud: A survey","volume":"102","author":"Monge","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gari, Y., Monge, D.A., Pacini, E., Mateos, C., and Garino, C.G. (2020). Reinforcement learning-based autoscaling of workflows in the cloud: A survey. arXiv.","DOI":"10.1016\/j.engappai.2021.104288"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.comcom.2023.06.010","article-title":"Auto-scaling techniques in container-based cloud and edge\/fog computing: Taxonomy and survey","volume":"209","author":"Dogani","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_19","first-page":"1","article-title":"A survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems","volume":"51","author":"Chen","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"80","DOI":"10.18517\/ijods.3.2.80-92.2022","article-title":"A cloud-based container microservices: A review on load-balancing and auto-scaling issues","volume":"3","author":"Rabiu","year":"2022","journal-title":"Int. J. Data Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"01115","DOI":"10.1051\/matecconf\/202439201115","article-title":"A review on fixed threshold based and adaptive threshold based auto-scaling techniques in cloud computing","volume":"392","author":"Khan","year":"2024","journal-title":"MATEC Web Conf."},{"key":"ref_22","first-page":"181","article-title":"Auto-scaling model for cloud computing system","volume":"5","author":"Hung","year":"2012","journal-title":"Int. J. Hybrid Inf. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Solino, A., Batista, T., and Cavalcante, E. (2023, January 20\u201323). Decision-Making Support to Auto-scale Smart City Platform Infrastructures. Proceedings of the 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), Aveiro, Portugal.","DOI":"10.23919\/CISTI58278.2023.10212058"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jannapureddy, R., Vien, Q.T., Shah, P., and Trestian, R. (2019). An auto-scaling framework for analyzing big data in the cloud environment. Appl. Sci., 9.","DOI":"10.3390\/app9071417"},{"key":"ref_25","first-page":"75","article-title":"Analyzing auto-scaling issues in cloud environments","volume":"14","author":"Alipour","year":"2014","journal-title":"CASCON"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"065","DOI":"10.30574\/gjeta.2024.18.2.0027","article-title":"Evaluating the impact of cloud computing on accounting firms: A review of efficiency, scalability, and data security","volume":"18","author":"Atadoga","year":"2024","journal-title":"Glob. J. Eng. Technol. Adv."},{"key":"ref_27","unstructured":"Mullapudi, M., Munjala, M.B., and Kulkarni, C. (2024, June 01). Designing a Resilient Parallel Distributed Task Infrastructure for Large-Scale Data Processing. Available online: https:\/\/www.researchgate.net\/profile\/Mahidhar-Mullapudi\/publication\/378438900_Designing_a_Resilient_Parallel_Distributed_Task_Infrastructure_for_Large-scale_Data_Processing\/links\/65d96d94adc608480ae7fa04\/Designing-a-Resilient-Parallel-Distributed-Task-Infrastructure-for-Large-scale-Data-Processing.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Singh, S.T., Tiwari, M., and Dhar, A.S. (2022, January 8\u201310). Machine Learning based Workload Prediction for Auto-scaling Cloud Applications. Proceedings of the 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON), Raigarh, India.","DOI":"10.1109\/OTCON56053.2023.10114033"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ashalatha, R., and Agarkhed, J. (2015). Evaluation of auto scaling and load balancing features in cloud. Int. J. Comput. Appl., 117.","DOI":"10.5120\/20561-2949"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"38575","DOI":"10.1109\/ACCESS.2024.3375772","article-title":"Proactive Auto-Scaling for Service Function Chains in Cloud Computing based on Deep Learning","volume":"12","author":"Taha","year":"2024","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ahamed, Z., Khemakhem, M., Eassa, F., Alsolami, F., and Al-Ghamdi, A.S.A.M. (2023). Technical study of deep learning in cloud computing for accurate workload prediction. Electronics, 12.","DOI":"10.3390\/electronics12030650"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pereira, P., Araujo, J., and Maciel, P. (2019, January 6\u20139). A hybrid mechanism of horizontal auto-scaling based on thresholds and time series. Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.","DOI":"10.1109\/SMC.2019.8914522"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1109\/TSC.2024.3354062","article-title":"Autoscaling Solutions for Cloud Applications under Dynamic Workloads","volume":"17","author":"Quattrocchi","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_34","unstructured":"Hu, Y., Deng, B., and Peng, F. (2016, January 14\u201317). Autoscaling prediction models for cloud resource provisioning. Proceedings of the 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e7943","DOI":"10.1002\/cpe.7943","article-title":"Energy-efficient sender-initiated threshold-based load balancing (e-STLB) in cloud computing environment","volume":"36","author":"Malla","year":"2024","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3603532","article-title":"Efficient Computation of Optimal Thresholds in Cloud Auto-scaling Systems","volume":"8","author":"Tournaire","year":"2023","journal-title":"ACM Trans. Model. Perform. Eval. Comput. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kushchazli, A., Safargalieva, A., Kochetkova, I., and Gorshenin, A. (2024). Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing. Mathematics, 12.","DOI":"10.3390\/math12030468"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Heimerson, A., Eker, J., and \u00c5rz\u00e9n, K.E. (2022, January 6\u20139). A Proactive Cloud Application Auto-Scaler using Reinforcement Learning. Proceedings of the 2022 IEEE\/ACM 15th International Conference on Utility and Cloud Computing (UCC), Vancouver, WA, USA.","DOI":"10.1109\/UCC56403.2022.00040"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"35464","DOI":"10.1109\/ACCESS.2021.3061890","article-title":"Intelligent autoscaling of microservices in the cloud for real-time applications","volume":"9","author":"Khaleq","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Joyce, J.E., and Sebastian, S. (2022, January 4\u20135). Reinforcement learning based autoscaling for kafka-centric microservices in kubernetes. Proceedings of the 2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS), Bangalore, India.","DOI":"10.1109\/PhDEDITS56681.2022.9955300"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alnawayseh, S.E., Muhammad, M.H.G., Hassan, Z., Fatima, M., Aslam, M.S., Ibrahim, A., and Ateeq, K. (2023, January 7\u20138). Resource Provisioning in Cloud Computing using Fuzzy Logic Control System: An adaptive approach. Proceedings of the 2023 International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates.","DOI":"10.1109\/ICBATS57792.2023.10111481"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14334","DOI":"10.1109\/ACCESS.2024.3357122","article-title":"An energy-aware task offloading and load balancing for latency-sensitive IoT applications in the Fog-Cloud continuum","volume":"12","author":"Mahapatra","year":"2024","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"39936","DOI":"10.1109\/ACCESS.2024.3376670","article-title":"A Novel Offloading Mechanism Leveraging Fuzzy Logic and Deep Reinforcement Learning to Improve IoT Application Performance in a Three-Layer Architecture within the Fog-Cloud Environment","volume":"12","author":"Abdulazeez","year":"2024","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Luan, S., and Shen, H. (2024, January 18\u201319). Minimize Resource Cost for Containerized Microservices Under SLO via ML-Enhanced Layered Queueing Network Optimization. Proceedings of the 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.","DOI":"10.1109\/Confluence60223.2024.10463310"},{"key":"ref_45","unstructured":"Jensen, A. (2024). AI-Driven DevOps: Enhancing Automation with Machine Learning in AWS. Integr. J. Sci. Technol., 1."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chhabra, M., and Arora, G. (2023, January 23\u201324). The Impact of Machine Learning (ML) Driven Algorithm Ranking and Visualization on Task Scheduling in Cloud Computing. Proceedings of the 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), Ghaziabad, India.","DOI":"10.1109\/AECE59614.2023.10428475"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yang, R., Ouyang, X., Chen, Y., Townend, P., and Xu, J. (2018, January 26\u201329). Intelligent resource scheduling at scale: A machine learning perspective. Proceedings of the 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE), Bamberg, Germany.","DOI":"10.1109\/SOSE.2018.00025"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Spantideas, S., Giannopoulos, A., Cambeiro, M.A., Trullols-Cruces, O., Atxutegi, E., and Trakadas, P. (2023, January 25\u201327). Intelligent Mission Critical Services over Beyond 5G Networks: Control Loop and Proactive Overload Detection. Proceedings of the 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), Istanbul, Turkey.","DOI":"10.1109\/SmartNets58706.2023.10216134"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pfister, B.J., Scheinert, D., Geldenhuys, M.K., and Kao, O. (2024). Daedalus: Self-Adaptive Horizontal Autoscaling for Resource Efficiency of Distributed Stream Processing Systems. arXiv.","DOI":"10.1145\/3629526.3645042"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gkontzis, A.F., Kotsiantis, S., Feretzakis, G., and Verykios, V.S. (2024). Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis. Big Data Cogn. Comput., 8.","DOI":"10.20944\/preprints202401.2226.v1"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Westergaard, G., Erden, U., Mateo, O.A., Lampo, S.M., Akinci, T.C., and Topsakal, O. (2024). Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets. Information, 15.","DOI":"10.3390\/info15010039"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Muccini, H., and Vaidhyanathan, K. (2019, January 25\u201326). A machine learning-driven approach for proactive decision making in adaptive architectures. Proceedings of the 2019 IEEE international conference on software architecture companion (ICSA-C), Hamburg, Germany.","DOI":"10.1109\/ICSA-C.2019.00050"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MNET.2013.6616117","article-title":"An auto-scaling mechanism for virtual resources to support mobile, pervasive, real-time healthcare applications in cloud computing","volume":"27","author":"Ahn","year":"2013","journal-title":"IEEE Netw."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.future.2011.05.009","article-title":"Model-driven auto-scaling of green cloud computing infrastructure","volume":"28","author":"Dougherty","year":"2012","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Grimson, M., Almeida, R., Shi, Q., Bai, Y., Angarita, H., Pacheco, F.S., Schmitt, R., Flecker, A., and Gomes, C.P. (2024, January 20\u201328). Scaling Up Pareto Optimization for Tree Structures with Affine Transformations: Evaluating Hybrid Floating Solar-Hydropower Systems in the Amazon. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i20.30210"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/TPDS.2024.3360448","article-title":"Multi-Agent Deep Reinforcement Learning Framework for Renewable Energy-Aware Workflow Scheduling on Distributed Cloud Data Centers","volume":"35","author":"Jayanetti","year":"2024","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Baynum, A., and Hao, W. (2024, January 8\u201310). Exploring the Impact of Cloud Computing and Edge Computing on Resource Consumption for Mobile Devices with Generative Artificial Intelligence APIs. Proceedings of the 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC60891.2024.10427798"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4261","DOI":"10.1109\/TSC.2023.3317262","article-title":"Auto-Scaling Containerized Applications in Geo-Distributed Clouds","volume":"16","author":"Shi","year":"2023","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_59","unstructured":"Meng, C., Tong, H., Wu, T., Pan, M., and Yu, Y. (2024). Multi-Level ML Based Burst-Aware Autoscaling for SLO Assurance and Cost Efficiency. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Choonhaklai, P., and Chantrapornchai, C. (2023, January 25\u201328). Two Autoscaling Approaches on Kubernetes Clusters Against Data Streaming Applications. Proceedings of the 2023 International Technical Conference on Circuits\/Systems, Computers, and Communications (ITC-CSCC), Jeju, Republic of Korea.","DOI":"10.1109\/ITC-CSCC58803.2023.10212432"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"109768","DOI":"10.1109\/ACCESS.2022.3214985","article-title":"Predictive hybrid autoscaling for containerized applications","volume":"10","author":"Vu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ju, L., Singh, P., and Toor, S. (2021, January 6\u20139). Proactive autoscaling for edge computing systems with kubernetes. Proceedings of the IEEE\/ACM International Conference on Utility and Cloud Computing Companion, Leicester, UK.","DOI":"10.1145\/3492323.3495588"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, Y., Guo, J., Fan, Y., Chen, L., Zhang, B., Wang, W., Zhao, Y., and Zhang, J. (August, January 31). On-demand provisioning of computing resources in computing power network with mixed CPU and GPU. Proceedings of the 2023 21st International Conference on Optical Communications and Networks (ICOCN), Qufu, China.","DOI":"10.1109\/ICOCN59242.2023.10236419"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1109\/TCE.2023.3320174","article-title":"Intelligent resource scaling for container based digital twin simulation of consumer electronics","volume":"70","author":"Jeon","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_65","first-page":"2808","article-title":"Liquid: Intelligent resource estimation and network-efficient scheduling for deep learning jobs on distributed GPU clusters","volume":"33","author":"Gu","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"30387","DOI":"10.1109\/ACCESS.2020.2973023","article-title":"An unsupervised deep learning model for early network traffic anomaly detection","volume":"8","author":"Hwang","year":"2020","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:43:54Z","timestamp":1760111034000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":66,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175551"],"URL":"https:\/\/doi.org\/10.3390\/s24175551","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,28]]}}}