Deep Learning and Feedback Control Based Container Auto-Scaling for Cloud Native Micro-Services

In Kubernetes-based Cloud Native platforms, allocating containers to micro-services elastically according to workload changes is benefical to minimizing resource cost while stabling response times. However, inaccurate performance models for multi-container systems, along with coarse-grained container-based allocation, cause performance fluctuations. In this paper, deep learning, traditional Jackson Queuing Network (JQN) and feedback control are integrated to devise a container provisioning algorithm which leverages the neural networks’ ability to fit nonlinear performance models, the real-time responsiveness of feedback control, and the precise prediction of micro-service interactions offered by the JQN. The proposal is evaluated on a real Kubernetes based Cloud Native cluster. Experimental results illustrate that the container cost is decreased by 10.94%<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>11.36% while satifisfying Service Level Agreements (SLA) in terms of 95^th accessing-path response times.