{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T23:53:13Z","timestamp":1771717993885,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For enhancing customer experience and accelerating job execution, IoT task offloading enables mobile end devices to release heavy computation and storage to the resource-rich nodes in collaborative Edges or Clouds. However, how different service architecture and offloading strategies quantitatively impact the end-to-end performance of IoT applications is still far from known particularly given a dynamic and unpredictable assortment of interconnected virtual and physical devices. This paper exploits potential network performance that manifests within the edge-cloud environment, then investigates and compares the impacts of two types of architectures: Loosely-Coupled (LC) and Orchestrator-Enabled (OE). Further, it introduces three customized offloading strategies in order to handle various requirements for IoT latency-sensitive applications. Through comparative experiments, we observed that the computational requirements exerts more influence on the IoT application\u2019s performance compared to the communication requirement. However, when the system scales up to accommodate more IoT devices, communication bandwidth will turn to be the dominant resource and becomes the essential factor that will directly impact the overall performance. Thus, orchestration is a necessary procedure to encompass optimized solutions under different constraints for optimal offloading placement.<\/jats:p>","DOI":"10.3390\/sym13030402","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T10:25:18Z","timestamp":1614594318000},"page":"402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Modeling and Analyzing Offloading Strategies of IoT Applications over Edge Computing and Joint Clouds"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7827-1826","authenticated-orcid":false,"given":"Jaber","family":"Almutairi","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Engineering, Taibah University, 12512 Al-Madinah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6772-700X","authenticated-orcid":false,"given":"Mohammad","family":"Aldossary","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, 11942 Al-Kharj, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,1]]},"reference":[{"key":"ref_1","unstructured":"(2020, August 13). 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