{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T04:06:29Z","timestamp":1769400389303,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62262011"],"award-info":[{"award-number":["62262011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["GuikeAB25069300"],"award-info":[{"award-number":["GuikeAB25069300"]}]},{"name":"Guangxi Science and Technology Program","award":["GuikeAB25069377"],"award-info":[{"award-number":["GuikeAB25069377"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Container-based cloud platforms enable flexible and lightweight application deployment, yet container scheduling remains challenged by resource fragmentation, load imbalance, excessive energy consumption, and service-level agreement (SLA) violations. To address these issues, this paper proposes a hybrid multi-objective optimization approach, termed HHO-GWO, which combines Harris Hawks Optimization (HHO) with the Grey Wolf Optimizer (GWO) for container initial placement in cloud environments. A unified fitness function is designed to jointly consider resource utilization, load balancing, resource fragmentation, energy consumption, and SLA violation rate. In addition, a dynamic weight adjustment mechanism and L\u00e9vy flight perturbation are incorporated to improve search adaptability and prevent premature convergence. The proposed method is evaluated through extensive simulations under different workload scales and compared with several representative metaheuristic algorithms. The results show that HHO-GWO achieves improved convergence behavior, solution quality, and stability, particularly in large-scale container deployment scenarios. These findings suggest that the proposed approach provides a practical and energy-aware solution for multi-objective container scheduling in cloud data centers.<\/jats:p>","DOI":"10.3390\/fi18010058","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T14:57:58Z","timestamp":1768921078000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Objective Optimization-Based Container Cloud Resource Scheduling Method"],"prefix":"10.3390","volume":"18","author":[{"given":"Danping","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolan","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhui","family":"Song","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3934","DOI":"10.1016\/j.jksuci.2021.03.002","article-title":"Container scheduling techniques: A Survey and assessment","volume":"34","author":"Ahmad","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10951-024-00820-1","article-title":"Scientific workflow scheduling algorithms in cloud environments: A comprehensive taxonomy, survey, and future directions","volume":"28","author":"Saeedizade","year":"2025","journal-title":"J. Sched."},{"key":"ref_3","first-page":"20240441","article-title":"Resource allocation strategies and task scheduling algorithms for cloud computing: A systematic literature review","volume":"34","author":"Nazri","year":"2025","journal-title":"J. Intell. Syst."},{"key":"ref_4","first-page":"2847","article-title":"Using light weight container a mesh based dynamic allocation task scheduling algorithm for cloud with IoT network","volume":"16","author":"Shakya","year":"2024","journal-title":"Int. J. Inf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1002\/spe.3195","article-title":"Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment","volume":"54","author":"Tang","year":"2024","journal-title":"Softw. Pract. Exp."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, H., Shen, C., Qiu, X., and Cheng, C. (2024). Container Scheduling Algorithms for Distributed Cloud Environments. Processes, 12.","DOI":"10.3390\/pr12091804"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s10723-024-09788-w","article-title":"Energy-aware Scheduling Algorithm for Microservices in Kubernetes Clouds","volume":"23","author":"Rao","year":"2024","journal-title":"J. Grid Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13677-019-0131-1","article-title":"A placement architecture for a container as a service (CaaS) in a cloud environment","volume":"8","author":"Hussein","year":"2019","journal-title":"J. Cloud Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1002\/spe.3376","article-title":"Multi-objective based container placement strategy in CaaS","volume":"55","author":"Khan","year":"2025","journal-title":"Softw. Pract. Exp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15222","DOI":"10.1007\/s10489-022-04164-1","article-title":"A priority-aware scheduling framework for heterogeneous workloads in container-based cloud","volume":"53","author":"Zhu","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9687","DOI":"10.1007\/s00521-023-08208-6","article-title":"A heuristic multi-objective task scheduling framework for container-based clouds via actor-critic reinforcement learning","volume":"35","author":"Zhu","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1109\/TCC.2018.2794344","article-title":"Locality-Aware Scheduling for Containers in Cloud Computing","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5959223","DOI":"10.1155\/2023\/5959223","article-title":"An Efficient Load Prediction-Driven Scheduling Strategy Model in Container Cloud","volume":"2023","author":"Wang","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.jnca.2019.04.003","article-title":"Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment","volume":"137","author":"Adhikari","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s10878-025-01265-8","article-title":"Energy-efficient real-time multi-workflow scheduling in container-based cloud","volume":"49","author":"Sun","year":"2025","journal-title":"J. Comb. Optim."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.future.2022.05.002","article-title":"A two-stage container management in the cloud for optimizing the load balancing and migration cost","volume":"135","author":"Zhang","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e8035","DOI":"10.1002\/cpe.8035","article-title":"Joint optimization of load balancing and resource allocation in cloud environment using optimal container management strategy","volume":"36","author":"Muniswamy","year":"2024","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Praveena, M.D.A., Sahu, B.J.R., Sharma, S., Kishan, R., and Dev, S. (2025, January 29\u201331). Task Scheduling with Optimized Load Balancing in Container-Based Cloud Computing Environments. Proceedings of the 2025 International Conference on Networks and Cryptology (NETCRYPT), New Delhi, India.","DOI":"10.1109\/NETCRYPT65877.2025.11102788"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e70157","DOI":"10.1002\/cpe.70157","article-title":"Container Placement Using Penalty-Based PSO in the Cloud Data Center","volume":"37","author":"Sahoo","year":"2025","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4698","DOI":"10.1109\/TNSM.2023.3264005","article-title":"VNF and CNF Placement in 5G: Recent Advances and Future Trends","volume":"20","author":"Attaoui","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111343","DOI":"10.1016\/j.comnet.2025.111343","article-title":"Containerized service placement and resource allocation at edge: A Hybrid Reinforcement Learning approach","volume":"267","author":"Zeng","year":"2025","journal-title":"Comput. Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.dcan.2023.02.012","article-title":"Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme","volume":"11","author":"Chen","year":"2025","journal-title":"Digit. Commun. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/TCC.2021.3137400","article-title":"Multi-Objective Multi-Factorial Evolutionary Algorithm for Container Placement","volume":"11","author":"Liu","year":"2023","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1109\/TNSM.2021.3135268","article-title":"Blender: A Container Placement Strategy by Leveraging Zipf-Like Distribution Within Containerized Data Centers","volume":"19","author":"Wu","year":"2022","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113719","DOI":"10.1016\/j.eswa.2020.113719","article-title":"A whale optimization system for energy-efficient container placement in data centers","volume":"164","author":"Luo","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, Q., Tan, X., Yang, J., Zheng, C., Wang, L., and Xu, Z. (2022, January 4\u20136). A Secure Container Placement Strategy Using Deep Reinforcement Learning in Cloud. Proceedings of the 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Hangzhou, China.","DOI":"10.1109\/CSCWD54268.2022.9776226"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kim, H., and Lee, C. (2025). Optimizing Container Placement in Data Centers by Deep Reinforcement Learning. Appl. Sci., 15.","DOI":"10.3390\/app15105720"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112129","DOI":"10.1016\/j.asoc.2024.112129","article-title":"Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment","volume":"165","author":"Suresh","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e13362","DOI":"10.1111\/exsy.13362","article-title":"Multi agent deep reinforcement learning for resource allocation in container-based clouds environments","volume":"42","author":"Nagarajan","year":"2025","journal-title":"Expert Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113917","DOI":"10.1016\/j.eswa.2020.113917","article-title":"An improved grey wolf optimizer for solving engineering problems","volume":"166","author":"Taghian","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8939","DOI":"10.1007\/s00521-021-05720-5","article-title":"Harris hawks optimization: A comprehensive review of recent variants and applications","volume":"33","author":"Alabool","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hussien, A.G., Abualigah, L., Abu Zitar, R., Hashim, F.A., Amin, M., Saber, A., Almotairi, K.H., and Gandomi, A.H. (2022). Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications. Electronics, 11.","DOI":"10.3390\/electronics11121919"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1007\/s10462-018-9634-2","article-title":"Recent studies on optimisation method of Grey Wolf Optimiser (GWO): A review (2014\u20132017)","volume":"52","author":"Hatta","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5579","DOI":"10.1007\/s11831-022-09780-1","article-title":"Harris Hawks Optimization Algorithm: Variants and Applications","volume":"29","author":"Shehab","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101795","DOI":"10.1016\/j.swevo.2024.101795","article-title":"Elite-driven grey wolf optimization for global optimization and its application to feature selection","volume":"92","author":"Zhang","year":"2025","journal-title":"Swarm Evol. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1007\/s11831-023-09928-7","article-title":"A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations","volume":"30","author":"Zamani","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mangalampalli, S., Karri, G.R., and Elngar, A.A. (2023). An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization. Sensors, 23.","DOI":"10.3390\/s23031384"},{"key":"ref_38","first-page":"1641","article-title":"Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing","volume":"71","author":"Kumar","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9696","DOI":"10.1016\/j.jksuci.2021.12.003","article-title":"Mantaray modified multi-objective Harris hawk optimization algorithm expedites optimal load balancing in cloud computing","volume":"34","author":"Haris","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/58\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T05:10:25Z","timestamp":1769317825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,20]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["fi18010058"],"URL":"https:\/\/doi.org\/10.3390\/fi18010058","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,20]]}}}