{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T18:41:53Z","timestamp":1781376113993,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.<\/jats:p>","DOI":"10.3390\/s23031384","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T01:27:58Z","timestamp":1674782878000},"page":"1384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1485-8783","authenticated-orcid":false,"given":"Sudheer","family":"Mangalampalli","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5177-8125","authenticated-orcid":false,"given":"Ganesh Reddy","family":"Karri","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6124-7152","authenticated-orcid":false,"given":"Ahmed A.","family":"Elngar","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef 62511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.4018\/IJWLTT.2021010102","article-title":"Integration of cloud computing, big data, artificial intelligence, and internet of things: Review and open research issues","volume":"16","author":"Saadia","year":"2021","journal-title":"Int. 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