Abstract
The resource scheduling of the container cloud system can be handled as a path planning problem. In response to the need for container resource scheduling that comprehensively considers the interests of users and service providers, this paper combines user quality of service (QoS) models and resource load balancing to study multi-objective container resource scheduling solutions, and proposes an improved Dynamic Adaptive Differential Evolution Algorithm (DADE), which adds adaptive changes to the mutation factor and crossover factor, and optimizes the mutation strategy and selection strategy, so that the algorithm has a broad solution space in the early stage; and a small-scale local search is carried out in the later stage, the resource scheduling strategy based on this algorithm is realized. Perform simulation experiments on the proposed algorithm and scheduling strategy. Experimental results show that the DADE algorithm is superior to mainstream heuristic algorithms in the evaluation of average function evaluation times, solution accuracy, convergence speed and other indicators. The resource scheduling effect has obvious advantages in task completion time, completion cost and resource load balancing.
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Acknowledgments
The work is supported by the Natural Science Foundation of China (61762008), the National Key Research and Development Project of China (2018YFB1404404), the Guangxi Natural Science Foundation Project (2017GXNSFAA198141), and the Major special project of science and technology of Guangxi (No. AA18118047-7).
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Hua, C., Chen, N., Xie, Y., Lian, L. (2021). Cooperative Scheduling Strategy of Container Resources Based on Improved Adaptive Differential Evolution Algorithm. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_31
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DOI: https://doi.org/10.1007/978-981-16-2540-4_31
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