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Volume 8 Issue 4
Apr.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(4): 848-865
Qing-Hua Zhu, Huan Tang, Jia-Jie Huang, and Yan Hou, "Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 848-865, Apr. 2021. doi: 10.1109/JAS.2021.1003934
Citation: Qing-Hua Zhu, Huan Tang, Jia-Jie Huang, and Yan Hou, "Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 848-865, Apr. 2021. doi: 10.1109/JAS.2021.1003934
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Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

doi: 10.1109/JAS.2021.1003934

  • School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

Funds:  This work was supported in part by the National Natural Science Foundation of China (61673123, 61603100), and in part by the Natural Science Foundation of Guangdong Province, China (2020A151501482)
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    Abstract
  • The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

     

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    Highlights

    • To guarantee security and reliability of applications in a multi-cloud environment.
    • Matching and Multi-round Allocation algorithm optimizes the makespan and total cost.
    • MMA algorithm covers: task-resource-matching and multiple rounds of resource reallocation.
    • MMA algorithm outperforms other benchmark algorithms.

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