{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:22:00Z","timestamp":1771003320044,"version":"3.50.1"},"reference-count":17,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2021,11,1]]},"abstract":"<jats:p>Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider\u2019s earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine\u2019s use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine\u2019s use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine\u2019s use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent.<\/jats:p>","DOI":"10.3233\/jcm-215225","type":"journal-article","created":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T12:53:25Z","timestamp":1627995205000},"page":"1575-1585","source":"Crossref","is-referenced-by-count":0,"title":["Resource clustering algorithm for cloud data centers"],"prefix":"10.1177","volume":"21","author":[{"given":"Caili","family":"Song","sequence":"first","affiliation":[]},{"given":"Bin","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Jiao","family":"Li","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/JCM-215225_ref1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.future.2020.05.040","article-title":"A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment","volume":"112","author":"Hassan","year":"2020","journal-title":"Future Generation Computer Systems"},{"key":"10.3233\/JCM-215225_ref2","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/TCC.2017.2662226","article-title":"Link-aware virtual machine placement for cloud services based on service-oriented architecture","volume":"8","author":"Tseng","year":"2020","journal-title":"IEEE Transactions on Cloud Computing"},{"key":"10.3233\/JCM-215225_ref3","doi-asserted-by":"crossref","unstructured":"S. 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