{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:44:43Z","timestamp":1769712283693,"version":"3.49.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Load balancing in cloud computing refers to dividing computing characteristics and workloads. Distributing resources among servers, networks, or computers enables enterprises to manage workload demands. This paper proposes a novel load-balancing method based on the Two-Level Particle Swarm Optimization (TLPSO). The proposed TLPSO-based load-balancing method can effectively solve the problem of dynamic load-balancing in cloud computing, as it can quickly and accurately adjust the computing resource distribution in order to optimize the system performance. The upper level aims to improve the population\u2019s diversity and escape from the local optimum. The lower level enhances the rate of population convergence to the global optimum while obtaining feasible solutions. Moreover, the lower level optimizes the solution search process by increasing the convergence speed and improving the quality of solutions. According to the simulation results, TLPSO beats other methods regarding resource utilization, makespan, and average waiting time.<\/jats:p>","DOI":"10.3233\/jifs-230828","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T12:18:30Z","timestamp":1690287510000},"page":"9433-9444","source":"Crossref","is-referenced-by-count":1,"title":["Load balancing in virtual machines of cloud environments using two-level particle swarm optimization algorithm"],"prefix":"10.1177","volume":"45","author":[{"given":"Chunrong","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Big Data, Chongqing Vocational College of Transportation, Jiangjin, Chongqing, China"}]},{"given":"Zhenghong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Big Data, Chongqing Vocational College of Transportation, Jiangjin, Chongqing, China"}]}],"member":"179","reference":[{"issue":"10","key":"10.3233\/JIFS-230828_ref2","doi-asserted-by":"crossref","first-page":"e4063","DOI":"10.1002\/ett.4063","article-title":"Kazem, Trustmanagement of services (TMoS): Investigating the current mechanisms","volume":"31","author":"Hayyolalam","year":"2020","journal-title":"Transactions on Emerging Telecommunications Technologies"},{"issue":"1-4","key":"10.3233\/JIFS-230828_ref3","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s00170-019-04213-z","article-title":"Exploring the state-of-the-art service composition approaches in cloud manufacturing systems to enhance upcoming techniques","volume":"105","author":"Hayyolalam","year":"2019","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"4","key":"10.3233\/JIFS-230828_ref5","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.3390\/su15043317","article-title":"Internet of Medical Things Privacy and Security: Challenges, Solutions, and Future Trends from a New Perspective","volume":"15","author":"Kamalov","year":"2023","journal-title":"Sustainability"},{"key":"10.3233\/JIFS-230828_ref7","doi-asserted-by":"crossref","first-page":"e538","DOI":"10.7717\/peerj-cs.538","article-title":"Low-diameter topic-based pub\/sub overlay network construction with minimum\u2013maximum node degree","volume":"7","author":"Yumusak","year":"2021","journal-title":"PeerJ Computer Science"},{"key":"10.3233\/JIFS-230828_ref11","doi-asserted-by":"crossref","first-page":"109076","DOI":"10.1016\/j.cie.2023.109076","article-title":"Simplified swarm optimization for hyperparameters of convolutional neural networks","volume":"177","author":"Yeh","year":"2023","journal-title":"Computers & Industrial Engineering"},{"key":"10.3233\/JIFS-230828_ref13","first-page":"2022","article-title":"Nejati, A. 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