Grey Markov model based horizontal scaling strategy for Kubernetes

With the rapid development and wide application of cloud computing technology, Kubernetes has attracted much attention as the most mainstream container cluster management tool. Aiming at the shortcomings of the horizontal auto-scaling strategy of Kubernetes itself, this paper proposes a horizontal auto-scaling optimization strategy based on the grey Markov model, which can effectively achieve fast scaling up to meet the sudden task requests and slow scaling down to reduce the SLA violation rate. Finally, the effectiveness of the algorithm is verified through experiments.