An IFWA-BSA Based Approach for Task Scheduling in Cloud Computing


  • Xiaoxia Li School of Artificial Intelligence and Big Data, Zibo Vocational Institute, Zibo, Shandong, 255000, China



Task scheduling model, cloud computing, chaotic inverse learning


Establishing an efficient cloud computing task scheduling model is the object of many scholars’ research. In view of the low scheduling efficiency in cloud computing task scheduling, we propose a cloud computing task scheduling algorithm based on the fusion of the Fireworks Algorithm and Bird Swarm Algorithm (IFWA-BSA). Firstly, we describe the cloud computing task scheduling model based on time and cost constraint functions, secondly, we use chaotic backward learning and Coasean distribution for optimization in FWA initialization; we set thresholds for the radius of core fireworks and non-core fireworks for optimization; we filter the IFWA individuals after each iteration by BSA algorithm, and finally, we use the IFWA-BSA algorithm is used in cloud computing task scheduling model to solve the optimal solution. In the simulation experiments, IFWA-BSA has obvious advantages over ACO, PSO and FWA in the comparison of execution time and consumption cost indexes, which reduces the scheduling time and cost of cloud computing.


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Author Biography

Xiaoxia Li, School of Artificial Intelligence and Big Data, Zibo Vocational Institute, Zibo, Shandong, 255000, China

Xiaoxia Li received her bachelor’s degree in computer science and technology from Liaocheng University in 2003 and her master’s degree in computer application and management from Qingdao University in 2013. She is currently a lecturer in the Artificial Intelligence and Big Data College of Zibo Vocational Institute, and her research interests are computer networks, computer applications, big data and cloud computing.


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How to Cite

Li, X. . (2023). An IFWA-BSA Based Approach for Task Scheduling in Cloud Computing. Journal of ICT Standardization, 11(01), 45–66.