{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T12:49:38Z","timestamp":1766407778001,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T00:00:00Z","timestamp":1625961600000},"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>\u201cHybrid Cloud Storage\u201d (HCS) is a widely adopted framework that combines the functionality of public and private cloud storage models to provide storage services. This kind of storage is especially ideal for organizations that seek to reduce the cost of their storage infrastructure with the use of \u201cPublic Cloud Storage\u201d as a backend to on-premises primary storage. Despite the higher performance, the hybrid cloud has latency issues, related to the distance and bandwidth of the public storage, which may cause a significant drop in the performance of the storage systems during data transfer. This issue can become a major problem when one or more private storage nodes fail. In this paper, we propose a new framework for optimizing the data uploading process that is currently used with hybrid cloud storage systems. The optimization is concerned with spreading the data over the multiple storages in the HCS system according to some predefined objective functions. Furthermore, we also used Network Coding technics for minimizing data transfer latency between the receiver (private storages) and transmitter nodes.<\/jats:p>","DOI":"10.3390\/fi13070181","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:23:36Z","timestamp":1626049416000},"page":"181","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Data Chunks Placement Optimization for Hybrid Storage Systems"],"prefix":"10.3390","volume":"13","author":[{"given":"Agil","family":"Yolchuyev","sequence":"first","affiliation":[{"name":"Department of Networked Systems and Services, Budapest University of Technology and Economics, Magyar Tudosok krt. 2, 1117 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janos","family":"Levendovszky","sequence":"additional","affiliation":[{"name":"Department of Networked Systems and Services, Budapest University of Technology and Economics, Magyar Tudosok krt. 2, 1117 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s11761-017-0218-9","article-title":"Cost-optimized redundant data storage in the cloud","volume":"11","author":"Waibel","year":"2017","journal-title":"Serv. 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