Abstract
In order to find the optimal big data balanced scheduling scheme under cloud computing and reduce the completion time of the task, an improved ant colony algorithm based algorithm for large data equalization scheduling under cloud computing was proposed. Firstly, a balanced scheduling algorithm structure was established, then the equilibrium problem to be explored was described, finally, the ant colony algorithm was used to simulate the ant search food process to solve the objective function. And the local and global information deep update methods was introduced to improve, speed up the search speed, and finally the performance test experiments on CloudSim simulation platform was performed. The results show that compared with the discrete particle swarm optimization (DPSO), the algorithm not only greatly reduces the execution time of cloud computing tasks (2.5 s), but also solves the problem of unbalanced data load, and achieves the balanced scheduling of large network data under cloud computing.
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Supported by Educational and Teaching Research and Reform Funds for Southwest Minzu University, “A Study on the Construction and Application of Teaching Resources in the SPOC Blended Teaching Mode” Project No.: 2018YB25.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ye, L. (2019). Research on Balanced Scheduling Algorithm of Big Data in Network Under Cloud Computing. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_21
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DOI: https://doi.org/10.1007/978-3-030-36402-1_21
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