基于长短期记忆神经网络的容器云队列在线任务动态分配

针对现有容器云在线任务分配方法分配合理性和资源均衡度较差、任务处理效率较低的问题，提出一种基于长短期记忆神经网络的容器云队列在线任务动态分配方法。描述容器云队列在线任务模型；以节点互补度、资源利用率以及能耗组成任务分配多目标函数；在约束条件下利用长短期记忆神经网络求解任务分配最优方案，完成容器云队列在线任务动态分配。实验结果表明，本文分配方案的分配合理性达到0.925，资源均衡度达到10.255，最长队列长度为10，最大能耗值为5000 W，分配合理性、资源均衡度、任务处理效率均得到改善，分配方案更加合理。

Container Cloud Queue Online Task Dynamic AllocationBased on Long Short-term Memory Neural Network

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.