Design of Kubernetes Scheduling Strategy Based on LSTM and Grey Model

摘要

As one of the popular container orchestration tools in the current community, kubernetes has been paid attention to the industry for its advanced design ideas and applied to the actual production environment. By analyzing the overall architecture and scheduling strategy of kubernetes, we proposes a method of optimizing the container scheduling algorithm by combining the grey system theory with the LSTM(Long Short-Term Memory) neural network prediction method in this paper. From the experience results we can see that algorithm can reduce the resource fragmentation problem of working nodes in the cluster and increase the utilization of cluster resources.