{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:57:20Z","timestamp":1774497440521,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Durban University of Technology"},{"name":"Innovate for African Universities"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The fast expansion of cloud computing has raised carbon emissions and energy usage in cloud data centers, so creative solutions for sustainable resource management are more necessary. This work presents a new algorithm\u2014Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement using Deep Q-Networks (DQNs) and Agglomerative Clustering (CARBON-DQN)\u2014that intelligibly balances environmental sustainability, service level agreement (SLA), and energy efficiency. The method combines a deep reinforcement learning model that learns optimum placement methods over time, carbon-aware data center profiling, and the hierarchical clustering of virtual machines (VMs) depending on resource constraints. Extensive simulations show that CARBON-DQN beats conventional and state-of-the-art algorithms like GRVMP, NSGA-II, RLVMP, GMPR, and MORLVMP very dramatically. Among many virtual machine configurations\u2014including micro, small, high-CPU, and extra-large instances\u2014it delivers the lowest carbon emissions, lowered SLA violations, and lowest energy usage. Driven by real-time input, the adaptive decision-making capacity of the algorithm allows it to dynamically react to changing data center circumstances and workloads. These findings highlight how well CARBON-DQN is a sustainable and intelligent virtual machine deployment system for cloud systems. To improve scalability, environmental effect, and practical applicability even further, future work will investigate the integration of renewable energy forecasts, dynamic pricing models, and deployment across multi-cloud and edge computing environments.<\/jats:p>","DOI":"10.3390\/computers14070280","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T15:01:05Z","timestamp":1752591665000},"page":"280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement in Cloud Data Centers Using Deep Q-Networks and Agglomerative Clustering"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9140-480X","authenticated-orcid":false,"given":"Maraga","family":"Alex","sequence":"first","affiliation":[{"name":"Information Technology, Accounting & Informatic, Durban University of Technology, 41\/43 M L Sultan Rd, Greyville, Durban 4001, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7197-8853","authenticated-orcid":false,"given":"Sunday O.","family":"Ojo","sequence":"additional","affiliation":[{"name":"Information Technology, Accounting & Informatic, Durban University of Technology, 41\/43 M L Sultan Rd, Greyville, Durban 4001, South Africa"}]},{"given":"Fred Mzee","family":"Awuor","sequence":"additional","affiliation":[{"name":"Computing Sciences, School of Information Science & Technology, Kisii University, P.O. Box 408, Kisii 40200, Kenya"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","first-page":"147","article-title":"Virtual Machine Placement Methods using Metaheuristic Algorithms in a Cloud Environment-A Comprehensive Review","volume":"22","author":"Alsadie","year":"2022","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.procs.2016.02.093","article-title":"A Survey of Virtual Machine Placement Techniques in a Cloud Data Center","volume":"78","author":"Usmani","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bharathi, P.D., Prakash, P., and Kiran, M.V.K. (2017). Energy efficient strategy for task allocation and virtual machine placement in cloud environment. 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