{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:02:07Z","timestamp":1772726527431,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T00:00:00Z","timestamp":1710806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry for Digital Transformation and Civil Service","award":["TSI-064200-2023-1"],"award-info":[{"award-number":["TSI-064200-2023-1"]}]},{"name":"European Union","award":["TSI-064200-2023-1"],"award-info":[{"award-number":["TSI-064200-2023-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.<\/jats:p>","DOI":"10.3390\/fi16030103","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T09:39:31Z","timestamp":1710841171000},"page":"103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent Resource Orchestration for 5G Edge Infrastructures"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9723-8100","authenticated-orcid":false,"given":"Rafael","family":"Moreno-Vozmediano","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2591-1719","authenticated-orcid":false,"given":"Rub\u00e9n S.","family":"Montero","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain"},{"name":"OpenNebula Systems, Paseo del Club Deportivo 1, Pozuelo de Alarc\u00f3n, 28223 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2227-2491","authenticated-orcid":false,"given":"Eduardo","family":"Huedo","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6230-8180","authenticated-orcid":false,"given":"Ignacio M.","family":"Llorente","sequence":"additional","affiliation":[{"name":"OpenNebula Systems, Paseo del Club Deportivo 1, Pozuelo de Alarc\u00f3n, 28223 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s10723-021-09545-3","article-title":"Opportunistic Deployment of Distributed Edge Clouds for Latency-Critical Applications","volume":"19","author":"Huedo","year":"2021","journal-title":"J. Grid Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s10723-019-09493-z","article-title":"Addressing Application Latency Requirements through Edge Scheduling","volume":"17","author":"Aral","year":"2019","journal-title":"J. Grid Comput."},{"key":"ref_3","unstructured":"Overbeek, D. (2024, February 26). Predictive DRS. Available online: https:\/\/blogs.vmware.com\/management\/2016\/11\/predictive-drs.html."},{"key":"ref_4","unstructured":"Kralicky, J. (2024, February 26). Opni-Multi-Cluster Observability with AIOps. Available online: https:\/\/opni.io\/."},{"key":"ref_5","unstructured":"Google Developers (2024, February 26). Google Active Assist. Available online: https:\/\/cloud.google.com\/recommender\/docs\/whatis-activeassist."},{"key":"ref_6","unstructured":"OpenNebula Systems (2024, February 26). ONEedge: An On-Demand Software-Defined Edge Computing Solution. Available online: https:\/\/oneedge.io\/."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/MIC.2011.44","article-title":"OpenNebula: A Cloud Management Tool","volume":"15","author":"Llorente","year":"2011","journal-title":"IEEE Internet Comput."},{"key":"ref_8","unstructured":"OpenNebula Systems (2024, January 25). OpenNebula: The Open Source Cloud & Edge Computing Platform. Available online: https:\/\/opennebula.io."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10473","DOI":"10.1109\/TVT.2023.3260988","article-title":"Energy-Efficient Optimization in Distributed Massive MIMO Systems for Slicing eMBB and URLLC Services","volume":"72","author":"Liu","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"197017","DOI":"10.1109\/ACCESS.2020.3034136","article-title":"Multi-Access Edge Computing: A Survey","volume":"8","author":"Filali","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127276","DOI":"10.1109\/ACCESS.2019.2938534","article-title":"Edge Computing in 5G: A Review","volume":"7","author":"Hassan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25329","DOI":"10.1109\/ACCESS.2023.3256522","article-title":"Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions","volume":"11","author":"Dakkak","year":"2023","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109720","DOI":"10.1016\/j.comnet.2023.109720","article-title":"Resource Allocation in Multi-access Edge Computing for 5G-and-beyond networks","volume":"227","author":"Sarah","year":"2023","journal-title":"Comput. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dai, Y., Xu, D., Zhang, K., Lu, Y., Maharjan, S., and Zhang, Y. (2019, January 16\u201319). Deep Reinforcement Learning for Edge Computing and Resource Allocation in 5G Beyond. Proceedings of the 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi\u2019an, China.","DOI":"10.1109\/ICCT46805.2019.8947146"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/TCCN.2019.2953061","article-title":"Intelligent Traffic Adaptive Resource Allocation for Edge Computing-Based 5G Networks","volume":"6","author":"Chen","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1109\/JIOT.2020.3011286","article-title":"A Machine Learning Approach for Task and Resource Allocation in Mobile-Edge Computing-Based Networks","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TETC.2019.2902661","article-title":"Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"28658","DOI":"10.1109\/ACCESS.2021.3059029","article-title":"Cost-Effective Resource Allocation for Multitier Mobile Edge Computing in 5G Mobile Networks","volume":"9","author":"Gazda","year":"2021","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1007\/s10586-019-03010-3","article-title":"A survey and classification of the workload forecasting methods in cloud computing","volume":"23","author":"Masdari","year":"2020","journal-title":"Clust. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yadav, A., Kushwaha, S., Gupta, J., Saxena, D., and Singh, A.K. (2022, January 10\u201311). A Survey of the Workload Forecasting Methods in Cloud Computing. Proceedings of the 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication (Lecture Notes in Electrical Engineering), Singapore.","DOI":"10.1007\/978-981-19-2828-4_49"},{"key":"ref_21","unstructured":"Rawat, P.S., and Gupta, P. (2022). Bio-Inspired Optimization in Fog and Edge Computing Environments, Auerbach Publications."},{"key":"ref_22","first-page":"1","article-title":"Resource Management Approaches in Fog Computing: A Comprehensive Review","volume":"18","author":"Souri","year":"2019","journal-title":"J. Grid Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gao, J., Wang, H., and Shen, H. (2020, January 3\u20136). Machine Learning Based Workload Prediction in Cloud Computing. Proceedings of the 29th International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA.","DOI":"10.1109\/ICCCN49398.2020.9209730"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s00607-022-01129-7","article-title":"Time series-based workload prediction using the statistical hybrid model for the cloud environment","volume":"105","author":"Devi","year":"2023","journal-title":"Computing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1093\/comjnl\/bxu024","article-title":"Resource Allocation in Cloud Computing Environments Based on Integer Linear Programming","volume":"58","author":"Rezvani","year":"2014","journal-title":"Comput. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3448","DOI":"10.1007\/s11227-021-03953-8","article-title":"Optimal machine placement based on improved genetic algorithm in cloud computing","volume":"78","author":"Lu","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103405","DOI":"10.1016\/j.jnca.2022.103405","article-title":"Machine learning (ML)-centric resource management in cloud computing: A review and future directions","volume":"204","author":"Khan","year":"2022","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gupta, P., Goyal, M.K., Chakraborty, S., and Elngar, A.A. (2022). Machine Learning and Optimization Models for Optimization in Cloud, Chapman and Hall\/CRC.","DOI":"10.1201\/9781003185376"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2449","DOI":"10.1109\/COMST.2022.3199544","article-title":"Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey","volume":"24","author":"Djigal","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Patsias, V., Amanatidis, P., Karampatzakis, D., Lagkas, T., Michalakopoulou, K., and Nikitas, A. (2023). Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature. Future Internet, 15.","DOI":"10.3390\/fi15080254"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.ins.2014.04.026","article-title":"The placement method of resources and applications based on request prediction in cloud data center","volume":"279","author":"Liang","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_32","first-page":"1","article-title":"Efficient resource provisioning for elastic Cloud services based on machine learning techniques","volume":"8","author":"Montero","year":"2019","journal-title":"J. Cloud Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mehmood, T., Latif, S., and Malik, S. (2018, January 8\u201310). Prediction Of Cloud Computing Resource Utilization. Proceedings of the 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), Islamabad, Pakistan.","DOI":"10.1109\/HONET.2018.8551339"},{"key":"ref_34","first-page":"10211","article-title":"A deep learning-based resource usage prediction model for resource provisioning in an autonomic cloud computing environment","volume":"34","author":"Bencherif","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100273","DOI":"10.1016\/j.iot.2020.100273","article-title":"Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research","volume":"12","author":"Aslanpour","year":"2020","journal-title":"Internet Things"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, J., Liu, C., Shang, Y., Mao, Z., and Chen, J. (July, January 28). Workload Predicting-Based Automatic Scaling in Service Clouds. Proceedings of the 2013 IEEE Sixth International Conference on Cloud Computing, Santa Clara, CA, USA.","DOI":"10.1109\/CLOUD.2013.146"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Di, S., Kondo, D., and Cirne, W. (2012, January 24\u201329). Host load prediction in a Google compute cloud with a Bayesian model. Proceedings of the 2012 International Conference for High Performance Computing, Networking, Storage and Analysis, Salt Lake City, UT, USA.","DOI":"10.1109\/SC.2012.68"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TCC.2014.2350475","article-title":"Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications\u2019 QoS","volume":"3","author":"Calheiros","year":"2015","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.future.2021.10.019","article-title":"Workload forecasting and energy state estimation in cloud data centres: ML-centric approach","volume":"128","author":"Khan","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s10723-021-09561-3","article-title":"Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters","volume":"19","author":"Nawrocki","year":"2021","journal-title":"J. Grid Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2020.11.011","article-title":"Integrated deep learning method for workload and resource prediction in cloud systems","volume":"424","author":"Bi","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13027","DOI":"10.1007\/s10489-021-03110-x","article-title":"Accurate workload prediction for edge data centers: Savitzky-Golay filter, CNN and BiLSTM with attention mechanism","volume":"52","author":"Chen","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.future.2011.07.003","article-title":"Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers","volume":"28","author":"Tordsson","year":"2012","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.future.2012.01.007","article-title":"Scheduling strategies for optimal service deployment across multiple clouds","volume":"29","author":"Montero","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_45","unstructured":"Entrialgo, J., D\u00edaz, J.L., Garc\u00eda, J., Garc\u00eda, M., and Garc\u00eda, D.F. (2017). Lecture Notes in Computer Science, Springer International Publishing."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102311","DOI":"10.1016\/j.simpat.2021.102311","article-title":"Modelling and simulation for cost optimization and performance analysis of transactional applications in hybrid clouds","volume":"109","author":"Entrialgo","year":"2021","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_47","unstructured":"Fadda, E., Plebani, P., and Vitali, M. (2016). Lecture Notes in Computer Science, Springer International Publishing."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/TSC.2019.2910069","article-title":"Monitoring-Aware Optimal Deployment for Applications Based on Microservices","volume":"14","author":"Fadda","year":"2021","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gong, Y. (2020, January 11\u201313). Optimal Edge Server and Service Placement in Mobile Edge Computing. Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC49862.2020.9339180"},{"key":"ref_50","unstructured":"Takeda, A., Kimura, T., and Hirata, K. (2020, January 7\u201310). Joint optimization of edge server and virtual machine placement in edge computing environments. Proceedings of the 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Auckland, New Zealand."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s10723-020-09507-1","article-title":"Workload Allocation in IoT-Fog-Cloud Architecture Using a Multi-Objective Genetic Algorithm","volume":"18","author":"Abbasi","year":"2020","journal-title":"J. Grid Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s12065-019-00233-6","article-title":"Genetic algorithm for quality of service based resource allocation in cloud computing","volume":"14","author":"Devarasetty","year":"2019","journal-title":"Evol. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s11277-020-07873-3","article-title":"Joint Resource Allocation at Edge Cloud Based on Ant Colony Optimization and Genetic Algorithm","volume":"117","author":"Xia","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_54","first-page":"4888","article-title":"A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment","volume":"34","author":"Pradhan","year":"2022","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.future.2021.07.023","article-title":"Energy-efficient VM scheduling based on deep reinforcement learning","volume":"125","author":"Wang","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1109\/TNSM.2021.3100460","article-title":"Resource Allocation in Data Centers Using Fast Reinforcement Learning Algorithms","volume":"18","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s10723-022-09603-4","article-title":"Scalable Virtual Machine Migration using Reinforcement Learning","volume":"20","author":"Hummaida","year":"2022","journal-title":"J. Grid Comput."},{"key":"ref_58","unstructured":"Prometheus authors (2024, January 25). Prometheus: From Metrics to Insight. Available online: https:\/\/prometheus.io."},{"key":"ref_59","unstructured":"Unit8 SA (2024, January 25). Darts: Time Series Made Easy in Python. Available online: https:\/\/unit8co.github.io\/darts."},{"key":"ref_60","unstructured":"Unit8 SA (2024, January 25). Darts Baseline Models. Available online: https:\/\/unit8co.github.io\/darts\/generated_api\/darts.models.forecasting.baselines.html."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (2008). Time Series Analysis, Wiley.","DOI":"10.1002\/9781118619193"},{"key":"ref_62","unstructured":"Smith, T.G. (2024, January 25). Alkaline-ML API Reference (pdmarima.arima.auto-arima). Available online: https:\/\/alkaline-ml.com\/pmdarima\/modules\/generated\/pmdarima.arima.auto_arima.html."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Box, G.E., and Tiao, G.C. (1992). Bayesian Inference in Statistical Analysis, John Wiley & Sons, Inc.","DOI":"10.1002\/9781118033197"},{"key":"ref_64","unstructured":"Scikit-Learn Developers (2024, January 25). Bayesian Ridge Regression. Available online: https:\/\/scikit-learn.org\/0.24\/modules\/linear_model.html#bayesian-ridge-regression."},{"key":"ref_65","unstructured":"Facebook Open Source (2024, January 25). Prophet: Forecasting at Scale. Available online: https:\/\/facebook.github.io\/prophet."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_67","unstructured":"Oreshkin, B.N., Carpov, D., Chapados, N., and Bengio, Y. (May, January 26). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_68","unstructured":"Wilkes, J. (2024, January 25). The Google Workload Cluster Traces. Available online: https:\/\/github.com\/google\/cluster-data."},{"key":"ref_69","unstructured":"Cortez, E. (2024, January 25). The Azure Public Dataset. Available online: https:\/\/github.com\/Azure\/AzurePublicDataset."},{"key":"ref_70","unstructured":"Delft University of Technology (2024, January 25). The Grid Workloads Archive: GWA-T-12 Bitbrains. Available online: http:\/\/gwa.ewi.tudelft.nl."},{"key":"ref_71","unstructured":"Ding, H. (2024, January 25). The Alibaba Cluster Trace Program. Available online: https:\/\/github.com\/alibaba\/clusterdata."},{"key":"ref_72","unstructured":"Nudler, E. (2024, January 25). Predator: Distributed Open-Source Performance Testing Platform for APIs. Available online: https:\/\/predator.dev."},{"key":"ref_73","unstructured":"Heyman, J., Bystr\u00f6m, C., Hamr\u00e9n, J., and Heyman, H. (2024, January 25). Locust: A modern load testing framework. Available online: https:\/\/locust.io."},{"key":"ref_74","unstructured":"Kolosov, O., Yadgar, G., Maheshwari, S., and Soljanin, E. (2020, January 25\u201326). Benchmarking in The Dark: On the Absence of Comprehensive Edge Datasets. Proceedings of the 3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20). USENIX Association, Santa Clara, CA, USA."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Tocze, K., Schmitt, N., Kargen, U., Aral, A., and Brandic, I. (2022, January 18\u201319). Edge Workload Trace Gathering and Analysis for Benchmarking. Proceedings of the 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC), Messina, Italy.","DOI":"10.1109\/ICFEC54809.2022.00012"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Cortez, E., Bonde, A., Muzio, A., Russinovich, M., Fontoura, M., and Bianchini, R. (2017, January 28\u201331). Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. Proceedings of the SOSP\u201917: 26th Symposium on Operating Systems Principles, Shanghai, China.","DOI":"10.1145\/3132747.3132772"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Shen, S., Beek, V.V., and Iosup, A. (2015, January 4\u20137). Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters. Proceedings of the 2015 15th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing, Shenzhen, China.","DOI":"10.1109\/CCGrid.2015.60"},{"key":"ref_78","unstructured":"Matthew, S., Stefan, V., Lou, H., John, F., Miles, L., Ted, R., Haroldo, G.S., Samuel, B., Bjarni, K., and Bruno, P. (2024, January 25). coin-or\/Cbc: Release Releases\/2.10.8. Available online: https:\/\/zenodo.org\/records\/6522795."},{"key":"ref_79","unstructured":"Cplex IBM ILOG (2009). V12.1: User\u2019s Manual for CPLEX. Int. Bus. Mach. Corp., 46, 157."},{"key":"ref_80","unstructured":"Bestuzheva, K., Besan\u00e7on, M., Chen, W.K., Chmiela, A., Donkiewicz, T., van Doornmalen, J., Eifler, L., Gaul, O., Gamrath, G., and Gleixner, A. (2021). The SCIP Optimization Suite 8.0. arXiv."},{"key":"ref_81","unstructured":"Google for Developers (2024, January 25). Get Started with OR-Tools for Python. Available online: https:\/\/developers.google.com\/optimization\/introduction\/python."},{"key":"ref_82","unstructured":"Google for Developers (2024, January 25). OR-Tools Python Reference: Linear Solver (Pywraplp Module). Available online: https:\/\/developers.google.com\/optimization\/reference\/python\/linear_solver\/pywraplp."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/3\/103\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:15:54Z","timestamp":1760105754000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/3\/103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,19]]},"references-count":82,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["fi16030103"],"URL":"https:\/\/doi.org\/10.3390\/fi16030103","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,19]]}}}