{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:36:32Z","timestamp":1775579792959,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Cloud resource provider deployment at random locations increases operational costs regardless of the application demand intervals. To provide adaptable load balancing under varying application traffic intervals, the auto-scaling concept has been introduced. This article introduces a Pervasive Auto-Scaling Method (PASM) for Computing Resource Allocation (CRA) to improve the application quality of service. In this auto-scaling method, deep reinforcement learning is employed to verify shared instances of up-scaling and down-scaling pervasively. The overflowing application demands are computed for their service failures and are used to train the learning network. In this process, the scaling is decided based on the maximum computing resource allocation to the demand ratio. Therefore, the learning network is also trained using scaling rates from the previous (completed) allocation intervals. This process is thus recurrent until maximum resource allocation with high sharing is achieved. The resource provider migrates to reduce the wait time based on the high-to-low demand ratio between successive computing intervals. This enhances the resource allocation rate without high wait times. The proposed method\u2019s performance is validated using the metrics resource allocation rate, service delay, allocated wait time, allocation failures, and resource utilization.<\/jats:p>","DOI":"10.3390\/bdcc9110294","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T16:49:51Z","timestamp":1763484591000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pervasive Auto-Scaling Method for Improving the Quality of Resource Allocation in Cloud Platforms"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4233-3425","authenticated-orcid":false,"given":"Vimal Raja","family":"Rajasekar","sequence":"first","affiliation":[{"name":"Department of Information Technology, Puducherry Technological University, Puducherry 605014, India"}]},{"given":"G.","family":"Santhi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Puducherry Technological University, Puducherry 605014, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3437","DOI":"10.1007\/s11227-022-04782-z","article-title":"Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism","volume":"79","author":"Dogani","year":"2023","journal-title":"J. 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