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.

翻译：随着数据在现代商业环境中重要性的日益提升，有效的数据管理与保护策略正获得越来越多的研究关注。云环境中的数据保护对于保障信息资产和维护持续性服务至关重要。本研究提出了一种集成Kubernetes管理平台与备份恢复工具的系统架构。该系统旨在即时检测灾难并自动从另一个Kubernetes集群恢复应用程序。实验结果表明，该系统可在无需人工干预的情况下15秒内完成恢复流程，实现快速恢复。与手动恢复流程相比，这显著减少了延迟和错误的可能性，从而提升了云环境中的数据管理和恢复效率。此外，我们的研究模型利用长短期记忆网络（LSTM）预测集群的CPU利用率。通过与无调度实验的对比，这种基于预测调度的必要性更加清晰，证明了其在防止性能退化方面的能力。本研究凸显了云环境中自动恢复系统的高效性与必要性，为未来研究指明了新方向。