{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:11:39Z","timestamp":1760134299833,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002471","name":"Dongguk University Research Fund of 2022","doi-asserted-by":"publisher","award":["S-2022-G0001-00070","2018R1A5A7023490","2021R1F1A1061514"],"award-info":[{"award-number":["S-2022-G0001-00070","2018R1A5A7023490","2021R1F1A1061514"]}],"id":[{"id":"10.13039\/501100002471","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["S-2022-G0001-00070","2018R1A5A7023490","2021R1F1A1061514"],"award-info":[{"award-number":["S-2022-G0001-00070","2018R1A5A7023490","2021R1F1A1061514"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the convergence of edge computing and sensor technologies has become a pivotal frontier revolutionizing real-time data processing. In particular, the practice of data acquisition\u2014which encompasses the collection of sensory information in the form of images and videos, followed by their transmission to a remote cloud infrastructure for subsequent analysis\u2014has witnessed a notable surge in adoption. However, to ensure seamless real-time processing irrespective of the data volume being conveyed or the frequency of incoming requests, it is vital to proactively locate resources within the cloud infrastructure specifically tailored to data-processing tasks. Many studies have focused on the proactive prediction of resource demands through the use of deep learning algorithms, generating considerable interest in real-time data processing. Nonetheless, an inherent risk arises when relying solely on predictive resource allocation, as it can heighten the susceptibility to system failure. In this study, a framework that includes algorithms that periodically monitor resource requirements and dynamically adjust resource provisioning to match the actual demand is proposed. Under experimental conditions with the Bitbrains dataset, setting the network throughput to 300 kB\/s and with a threshold of 80%, the proposed system provides a 99% performance improvement in terms of the autoscaling algorithm and requires only 0.43 ms of additional computational overhead compared to relying on a simple prediction model alone.<\/jats:p>","DOI":"10.3390\/s23239436","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T03:48:17Z","timestamp":1701056897000},"page":"9436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Autoscaling System Based on Predicting the Demand for Resources and Responding to Failure in Forecasting"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3549-347X","authenticated-orcid":false,"given":"Jieun","family":"Park","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4963-0057","authenticated-orcid":false,"given":"Junho","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39824","DOI":"10.1109\/ACCESS.2023.3269297","article-title":"Innovative trends in the 6G era: A comprehensive survey of architecture, applications, technologies, and challenges","volume":"11","author":"Khanh","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4347","DOI":"10.1109\/TMC.2022.3150401","article-title":"WAVE: Edge-device cooperated real-time object detection for open-air applications","volume":"22","author":"Dong","year":"2022","journal-title":"IEEE Trans. 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