{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:16:01Z","timestamp":1780355761523,"version":"3.54.1"},"reference-count":91,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,4]],"date-time":"2020-04-04T00:00:00Z","timestamp":1585958400000},"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>Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.<\/jats:p>","DOI":"10.3390\/sym12040551","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T03:40:19Z","timestamp":1586403619000},"page":"551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing"],"prefix":"10.3390","volume":"12","author":[{"given":"Mazen","family":"Farid","sequence":"first","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"},{"name":"Faculty of Education-Saber, University of Aden, Aden 2408, Yemen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohaya","family":"Latip","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"},{"name":"Institute for Mathematical Research (INSPEM), Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masnida","family":"Hussin","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nor Asilah Wati","family":"Abdul Hamid","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.jnca.2018.03.028","article-title":"Multi-objective scheduling for scientific workflow in multicloud environment","volume":"114","author":"Hu","year":"2018","journal-title":"J. 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