Research on Balanced Scheduling Algorithm of Big Data in Network Under Cloud Computing

  • Lunqiang YeEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 301)


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.


Cloud computing Big data Balanced scheduling Ant colony algorithm 



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.


  1. 1.
    Ying, T.J.: Multi source heterogeneous data scheduling algorithm under cloud computing. Sci. Technol. Eng. 21(34), 268–272 (2017)Google Scholar
  2. 2.
    Jinfang, Z., Qingxin, W., Jiaman, D., et al.: A big data dynamic migration strategy in cloud computing environment. Comput. Eng. 42(5), 13–17 (2016)Google Scholar
  3. 3.
    Luo Nan Super Cloud: The optimization of resource scheduling algorithm for balancing load under cloud computing. Sci. Technol. Eng. 16(34), 86–91 (2017)Google Scholar
  4. 4.
    Xin, L.: Cloud computing communication network information download balanced scheduling optimization research. Comput. Simulation 33(10), 162–165 (2016)Google Scholar
  5. 5.
    Xiaofeng, L.: Research and improvement of cloud resource scheduling method in cloud computing optical fiber network. Laser J. 37(5), 99–103 (2016)Google Scholar
  6. 6.
    Junying, W., Xinrui, C.X.: Load balancing and efficient scheduling method for diversity resources in cloud computing environment. Bull. Sci. Technol. 33(12), 167–170 (2017)Google Scholar
  7. 7.
    Kai, Z.: Research on cloud computing platform service resource scheduling. Comput. Simulation 34(9), 424–427 (2017)Google Scholar
  8. 8.
    Xiaonian, W., Xin, Z., Mengchuan, et al.: Two-phase task scheduling algorithm for multi-objective in cloud computing. Comput. Eng. Des. 38(6), 1551–1555 (2017)Google Scholar
  9. 9.
    Han, H., Peng, W., Kun, C., et al.: Task scheduling algorithm for cloud computing based on multi-scale quantum harmonic oscillator algorithm. J. Comput. Appl. 37(7), 1888–1892 (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Southwest Minzu UniversityChengduChina

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