关键词: 长短期记忆神经网络, 容器云, 任务分配, 多目标函数, 约束条件

Abstract: Aiming at the problems of the poor rationality of allocation and resource balance degree and the low task processing efficiency of existing container cloud online task allocation methods, a dynamic online task allocation method of container cloud queue based on long short-term memory neural network is proposed. This paper describes the online task model of container cloud queue, assigns multi-objective functions with node complementarity, resource utilization ratio and energy consumption composition, solves the optimal task allocation scheme with long short-term memory neural network under the constraint condition, and completes the dynamic task allocation of container cloud queue. The experimental results show that the allocation rationality of the allocation scheme proposed in this paper reaches 0.925, the resource balance degree reaches 10.255, the longest queue length is 10, and the maximum energy consumption value is 5000 W. The allocation rationality, resource balance degree and task processing efficiency are all improved, and the allocation scheme is more reasonable.

Key words: long short-term memory neural network, container cloud, task assignment, multi objective function, constraint condition