{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T23:23:14Z","timestamp":1770160994192,"version":"3.49.0"},"reference-count":113,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Cloud computing utilization has experienced progressive expansion over the last decade, which has raised concerns and challenges regarding efficient resource allocation and energy efficiency. The burgeoning increase in the number of cloud computing users and their data exacerbates the difficulty of resolving these challenges using conventional methods. Thus, utilizing intelligent approaches is indispensable. Among the most recent intelligent methods, artificial intelligence-based techniques have gained prominence across numerous research domains, including cloud resource management. Through a literature review aimed at analyzing existing studies addressing the open challenges of cloud computing, we have identified some gaps that are presented in this paper. Moreover, this paper presents a survey on cloud resource management solutions spanning from 2018 to 2025, with a focus on the papers that utilized intelligent methodologies for green computing. More specifically, this study shed light on the prevailing challenges in the field concerning methods, research areas, metrics, tools, and datasets. Furthermore, it provides a clear classification of methods, research areas, and metrics.<\/jats:p>","DOI":"10.3390\/fi18020076","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T10:03:29Z","timestamp":1770113009000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Intelligent Resource Management Solutions for Green Cloud Computing"],"prefix":"10.3390","volume":"18","author":[{"given":"Amirmohammad","family":"Parhizkar","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Tarbiat Modarres University, Tehran 14155-3961, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8215-7797","authenticated-orcid":false,"given":"Ehsan","family":"Arianyan","sequence":"additional","affiliation":[{"name":"ICT Research Institute, Tehran 14155-3961, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6173-1577","authenticated-orcid":false,"given":"Pejman","family":"Goudarzi","sequence":"additional","affiliation":[{"name":"ICT Research Institute, Tehran 14155-3961, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,1]]},"reference":[{"key":"ref_1","unstructured":"Mohamed, H., Alkabani, Y., and Selmy, H. 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