{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:58:49Z","timestamp":1763348329853},"reference-count":35,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper is aimed at efficiently distributing workload between the Fog Layer and the Cloud Network and then optimizing resource allocation in cloud networks to ensure better utilization and quick response time of the resources available to the end user. We have employed a Dead-line aware scheme to migrate the data between cloud and Fog networks based on data profiling and then used K-Means clustering and Service-request prediction model to allocate the resources efficiently to all requests. To substantiate our model, we have used iFogSim, which is an extension of the CloudSim simulator. The results clearly show that when an optimized network is used the Quality of Service parameters exhibit better efficiency and output.<\/jats:p>","DOI":"10.1515\/comp-2020-0162","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T23:10:28Z","timestamp":1614035428000},"page":"262-274","source":"Crossref","is-referenced-by-count":10,"title":["QoS Based Optimal Resource Allocation and Workload Balancing for Fog Enabled IoT"],"prefix":"10.1515","volume":"11","author":[{"given":"Adnan","family":"Khalid","sequence":"first","affiliation":[{"name":"Department of Computer Science , Government College University , Lahore , Pakistan"}]},{"given":"Qurat","family":"ul Ain","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Government College University , Lahore , Pakistan"}]},{"given":"Awais","family":"Qasim","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Government College University , Lahore , Pakistan ; School of Science, Engineering and Environment , University of Salford , United Kingdom of Great Britain and Northern Ireland ; Email: A.qasim2@salford.ac.uk"}]},{"given":"Zeeshan","family":"Aziz","sequence":"additional","affiliation":[{"name":"School of Science, Engineering and Environment , University of Salford , United Kingdom of Great Britain and Northern Ireland"}]}],"member":"374","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"2022020121510210806_j_comp-2020-0162_ref_001","doi-asserted-by":"crossref","unstructured":"Clohessy T., Acton T., Morgan L., Smart city as a service (SCaaS): A future roadmap for e-government smart city cloud computing initiatives, Proceedings of the 7th IEEE\/ACM International Conference on Utility and Cloud Computing, 2014, 836\u2013841","DOI":"10.1109\/UCC.2014.136"},{"key":"2022020121510210806_j_comp-2020-0162_ref_002","doi-asserted-by":"crossref","unstructured":"Zaman S., Grosu D., Combinatorial auction-based allocation of virtual machine instances in clouds, Journal of Parallel and Distributed Computing, 2013, 73(4), 495\u2013508","DOI":"10.1016\/j.jpdc.2012.12.006"},{"key":"2022020121510210806_j_comp-2020-0162_ref_003","doi-asserted-by":"crossref","unstructured":"\u00d6zer A. 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