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

作者：Zhicheng Cai, Hang Wu, Xu Jiang, Xiaoping Li, Rajkumar Buyya · 发表于：IEEE Transactions on Services Computing · 年份：2025 · DOI：10.1109/tsc.2025.3596887 · 被引用次数：2 · 研究领域：Cloud Computing and Resource Management

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%$\sim$11.36% while satifisfying Service Level Agreements (SLA) in terms of 95thaccessing-path response times.