Abstract
To solve the scheduling problem of workflow tasks in cloud computing, this paper combined the improved fuzzy c-means clustering algorithm (IFCM) and the improved ant colony optimization algorithm (IACO) and proposed a new workflow task scheduling algorithm. Firstly, the proposed algorithm used the IFCM to classify resources. Then, tasks will be sorted by their priority. Based on the results of resource clustering and the distance between resources and expect of tasks, tasks will be assigned to the appropriate resources and the scheduling will be initialized. After that, the workflow tasks will be encoded based on the initial scheduling. At last, ant colony optimization algorithm will be improved by the cross and mutation operation in genetic algorithm and used to search optimal schedules. The experiments showed that the proposed algorithm could quickly and efficiently find appropriate scheduling scheme, effectively reduce the time span of workflow tasks and increase the utilization of resources.
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Acknowledgement
This work was supported in part by National Science Foundation of China (No. 61572259, No. U1736105) and Special Public Sector Research Program of China (No. GYHY201506080) and was also supported by PAPD.
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Liu, Q., Ma, T., Li, J., Shen, W. (2018). Workflow Task Scheduling Algorithm Based on IFCM and IACO. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_35
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DOI: https://doi.org/10.1007/978-3-030-00009-7_35
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