Design and Implementation of an Automated Disaster-recovery System for a Kubernetes Cluster Using LSTM
This addresses data protection and recovery efficiency for cloud-based businesses, though it is incremental as it builds on existing Kubernetes and LSTM methods.
The study tackled automated disaster recovery in Kubernetes clusters by integrating management platforms with backup tools, achieving restoration within 15 seconds without human intervention. It also used LSTM to predict CPU utilization, preventing performance degradation compared to unscheduled approaches.
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.