  With the increasing importance of data in the modern business environment,
effective data man-agement and protection strategies are gaining increasing
research attention. Data protection in a cloud environment is crucial for
safeguarding information assets and maintaining sustainable services. This
study introduces a system structure that integrates Kubernetes management
plat-forms with backup and restoration tools. This system is designed to
immediately detect disasters and automatically recover applications from
another kubernetes cluster. The experimental results show that this system
executes the restoration process within 15 s without human intervention,
enabling rapid recovery. This, in turn, significantly reduces the potential for
delays and errors compared with manual recovery processes, thereby enhancing
data management and recovery ef-ficiency in cloud environments. Moreover, our
research model predicts the CPU utilization of the cluster using Long
Short-Term Memory (LSTM). The necessity of scheduling through this predict is
made clearer through comparison with experiments without scheduling,
demonstrating its ability to prevent performance degradation. This research
highlights the efficiency and necessity of automatic recovery systems in cloud
environments, setting a new direction for future research.
