{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:35:56Z","timestamp":1767206156953,"version":"build-2238731810"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Umm Al-Qura University, Saudi Arabia","award":["25UQU4331451GSSR01"],"award-info":[{"award-number":["25UQU4331451GSSR01"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Algorithms"],"abstract":"<jats:p>Cloud computing has transformed modern IT infrastructure by enabling scalable, on-demand access to virtualized resources. However, the rapid growth of cloud services has intensified energy consumption across data centres, increasing operational costs and carbon footprints. Traditional load-balancing methods, such as Round Robin and First-Fit, often fail to adapt dynamically to fluctuating workloads and heterogeneous resources. To address these limitations, this study introduces a Reinforcement Learning-guided hybrid optimization framework that integrates the Black Eagle Optimizer (BEO) for global exploration with the Pelican Optimization Algorithm (POA) for local refinement. A lightweight RL controller dynamically tunes algorithmic parameters in response to real-time workload and utilization metrics, ensuring adaptive and energy-aware scheduling. The proposed method was implemented in CloudSim 3.0.3 and evaluated under multiple workload scenarios (ranging from 500 to 2000 cloudlets and up to 32 VMs). Compared with state-of-the-art baselines, including PSO-ACO, MS-BWO, and BSO-PSO, the RL-enhanced hybrid BEO\u2013POA achieved up to 30.2% lower energy consumption, 45.6% shorter average response time, 28.4% higher throughput, and 12.7% better resource utilization. These results confirm that combining metaheuristic exploration with RL-based adaptation can significantly improve the energy efficiency, responsiveness, and scalability of cloud scheduling systems, offering a promising pathway toward sustainable, performance-optimized data-centre management.<\/jats:p>","DOI":"10.3390\/a18110715","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T14:37:52Z","timestamp":1763131072000},"page":"715","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reinforcement Learning-Guided Hybrid Metaheuristic for Energy-Aware Load Balancing in Cloud Environments"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4442-1865","authenticated-orcid":false,"given":"Yousef","family":"Sanjalawe","sequence":"first","affiliation":[{"name":"Department of Information Technology, King Abdullah II School for Information Technology, University of Jordan (UJ), Amman 11942, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2134-4158","authenticated-orcid":false,"given":"Salam","family":"Al-E\u2019mari","sequence":"additional","affiliation":[{"name":"Department of Information Security, Faculty of Information Technology, University of Petra (UoP), Amman 11196, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0424-6894","authenticated-orcid":false,"given":"Budoor","family":"Allehyani","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computing, Umm Al-Qura University (UQU), Makkah 24381, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-7998","authenticated-orcid":false,"given":"Sharif Naser","family":"Makhadmeh","sequence":"additional","affiliation":[{"name":"Department of Information Technology, King Abdullah II School for Information Technology, University of Jordan (UJ), Amman 11942, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Cloud Datacenter Selection Using Service Broker Policies: A Survey","volume":"139","author":"Sanjalawe","year":"2024","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_2","first-page":"3179","article-title":"Cloud Data Center Selection Using a Modified Differential Evolution","volume":"69","author":"Sanjalawe","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1007\/s10586-022-03713-0","article-title":"Energy efficiency in cloud computing data centers: A survey on software technologies","volume":"26","author":"Katal","year":"2023","journal-title":"Clust. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1007\/s12652-020-01747-z","article-title":"Cloud computing using load balancing and service broker policy for IT service: A taxonomy and survey","volume":"11","author":"Jyoti","year":"2020","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1002\/spe.3248","article-title":"Energy-efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads","volume":"54","author":"Buyya","year":"2024","journal-title":"Softw. Pract. Exp."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103885","DOI":"10.1016\/j.jnca.2024.103885","article-title":"Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation","volume":"226","author":"Rozehkhani","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_7","first-page":"580","article-title":"A Review exploration of Load Balancing Techniques in Cloud Computing","volume":"30","author":"Gupta","year":"2024","journal-title":"Educ. Adm. Theory Pract."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s10462-024-10925-w","article-title":"A systematic literature review for load balancing and task scheduling techniques in cloud computing","volume":"57","author":"Devi","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109712","DOI":"10.1016\/j.compeleceng.2024.109712","article-title":"Adaptive workload management in cloud computing for service level agreements compliance and resource optimization","volume":"120","author":"Ghandour","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_10","unstructured":"Agarwal, S., Singh, J., and Ansari, M. (2022, January 28\u201329). Recent developments of load balancing in cloud computing: A review. Proceedings of the AIP Conference Proceedings, Delhi, India."},{"key":"ref_11","first-page":"12","article-title":"Optimization of Cloud Computing Resources in Japan","volume":"7","author":"Sakamoto","year":"2024","journal-title":"Am. J. Comput. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Simaiya, S., Lilhore, U.K., Sharma, Y.K., Rao, K.B., Maheswara Rao, V., Baliyan, A., Bijalwan, A., and Alroobaea, R. (2024). A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-51466-0"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khan, A.R. (2024). Dynamic Load Balancing in Cloud Computing: Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling. Processes, 12.","DOI":"10.3390\/pr12030519"},{"key":"ref_14","first-page":"100948","article-title":"Load balancing in cloud computing via intelligent PSO-based feedback controller","volume":"41","author":"Ghafir","year":"2024","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shankar, J., Hussain, I., Zafar, S., Khan, I.R., and Khalique, A. (2024, January 23\u201324). Effective Resource Allocation and Load Balancing in Green Cloud Computing. Proceedings of the International Conference on ICT for Digital, Smart, and Sustainable Development, Delhi, India.","DOI":"10.1007\/978-981-97-7831-7_26"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100620","DOI":"10.1016\/j.cosrev.2024.100620","article-title":"Sustainable computing across datacenters: A review of enabling models and techniques","volume":"52","author":"Zakarya","year":"2024","journal-title":"Comput. Sci. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"490","DOI":"10.12694\/scpe.v26i1.3955","article-title":"Energy and Deadline Aware Workflow Scheduling using Adaptive Remora Optimization in Cloud Computing","volume":"26","author":"Srivastava","year":"2025","journal-title":"Scalable Comput. Pract. Exp."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1007\/s40031-024-01139-3","article-title":"Optimizing Resource Utilization and Improving Performance in Cloud Computing Through PSO-Based Scheduling and ACO-Based Load Balancing","volume":"106","author":"Jie","year":"2024","journal-title":"J. Inst. Eng. Ser. B"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bhattacharya, T., Tanniru, V., Majumder, S., and Veeramalla, S. (2024, January 6\u20139). Enhancing the Energy Efficiency with DURGA, a Novel Geographical Load Balancer. Proceedings of the 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), Philadelphia, PA, USA.","DOI":"10.1109\/CCGridW63211.2024.00029"},{"key":"ref_20","first-page":"100894","article-title":"An energy efficient and secure model using chaotic levy flight deep Q-learning in healthcare system","volume":"39","author":"Gowri","year":"2023","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s12652-021-03429-w","article-title":"ABSO: An energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment","volume":"14","author":"Hariharan","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_22","first-page":"6305","article-title":"Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers","volume":"75","author":"Khan","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gnanaprakasam, D., Mohanraj, M., Srinivas, T.A.S., Bhaggiaraj, S., Baskaran, J., and Sivankalai, S. (2023, January 29\u201330). Efficient Task Scheduling in Cloud Environment Based on Hyper Min Max Task Scheduling. Proceedings of the 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballar, India.","DOI":"10.1109\/ICDCECE57866.2023.10150869"},{"key":"ref_24","first-page":"4397","article-title":"Cost-efficient resource scheduling in cloud for big data processing using metaheuristic search black widow optimization (MS-BWO) algorithm","volume":"44","author":"Balasubramanian","year":"2023","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s11277-021-09133-4","article-title":"P2BED-C: A novel peer to peer load balancing and energy efficient technique for data-centers over cloud","volume":"123","author":"Kumar","year":"2022","journal-title":"Wirel. Pers. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aldossary, M., Alharbi, H.A., and Ayub, N. (2024). Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems. Comput. Mater. Contin., 79.","DOI":"10.32604\/cmc.2024.050862"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12361","DOI":"10.1007\/s10586-024-04586-1","article-title":"Black eagle optimizer: A metaheuristic optimization method for solving engineering optimization problems","volume":"27","author":"Zhang","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4081","DOI":"10.1007\/s00521-021-06747-4","article-title":"Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: A comprehensive survey, applications, comparative analysis, and results","volume":"34","author":"Abualigah","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Trojovsk\u1ef3, P., and Dehghani, M. (2022). Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22.","DOI":"10.3390\/s22030855"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1007\/s11831-024-10168-6","article-title":"Metaheuristics for solving global and engineering optimization problems: Review, applications, open issues and challenges","volume":"31","author":"Houssein","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Khan, A., Bressel, M., Davigny, A., Abbes, D., and Ould Bouamama, B. (2025). Comprehensive Review of Hybrid Energy Systems: Challenges, Applications, and Optimization Strategies. Energies, 18.","DOI":"10.3390\/en18102612"},{"key":"ref_34","first-page":"279","article-title":"Q-learning","volume":"8","author":"Watkins","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_35","unstructured":"Sutton, R., and Barto, A. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1023\/A:1022689125041","article-title":"Asynchronous stochastic approximation and Q-learning","volume":"16","author":"Tsitsiklis","year":"1994","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liberzon, D. (2003). Switching in Systems and Control, Birkh\u00e4user.","DOI":"10.1007\/978-1-4612-0017-8"}],"updated-by":[{"DOI":"10.3390\/a18120799","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000}}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/715\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:51:31Z","timestamp":1765961491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":37,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["a18110715"],"URL":"https:\/\/doi.org\/10.3390\/a18110715","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,13]]}}}