Design and Implementation of an Automated Disaster-recovery System for a Kubernetes Cluster Using LSTM

TL;DR

This paper presents an automated disaster recovery system for Kubernetes clusters that uses LSTM to predict CPU utilization, enabling rapid, automatic recovery within 15 seconds and improving data protection in cloud environments.

Contribution

It introduces a novel integrated system combining Kubernetes management with LSTM-based prediction for automatic disaster recovery, reducing recovery time and preventing performance degradation.

Findings

Recovery process completed within 15 seconds without human intervention

LSTM prediction effectively prevents performance degradation

System enhances data management and recovery efficiency in cloud environments

Abstract

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.