## 2 Related Work
It addresses application recovery strategies and experimentally evaluates recovery time and capability. Compared to the technique used in the work presented in [14], which required standby server resources, our research enhances resource-utilization efficiency by conducting recovery in operational clusters. [PERSON] et al. [14] addressed automatic recovery from the functional loss of applications in the same environment, whereas we propose cluster-level automatic recovery, which automatically restores applications to a different cluster when the original cluster loses functionality. In the realm of commercial cluster and cloud management, AWS Elastic Disaster Recovery (AWS DRS) [18] and Google Cloud's disaster recovery [19] methods are designed to enhance the resilience and availability of systems and applications in cloud environments. By leveraging these services, businesses can achieve minimal downtime and data loss in the event of outages or disasters, aligning with the Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs), which are critical for business continuity management. To meet the RTOs and RPOs, AWS DRS continuously synchronizes data across multiple servers to enable rapid recovery, meeting the RTOs and RPOs in the event of a disaster. This is usually called active/active strategy because a source and target cluster are always active for real-time synchronization. However, this leads to additional resource use and increased costs. Google Cloud's disaster recovery supports similar features. In our study, we assume a cluster environment, deploying an active/passive strategy where backup data is restored to the target cluster only when there is a problem with the source cluster. Three distinctive features are as follows: (1) source cluster data are continuously transferred to backup storage to meet RPO; (2) target clusters are not in a standby state and are not only dedicated to a source cluster restore. They may have up-and-running services. (3) This strategy does not require complex data synchronization or migration before the recovery process, thus allowing for more efficient resource utilization. Our proposed method and module can be integrated with a snapshot-based recovery method, which creates and stores a copy of the data at a specific point in time, allowing for the system to be quickly restored when necessary. This offers the advantage of cost and resource efficiency while meeting the RTO and RPO requirements, although it may not be as efficient as real-time recovery. Nonetheless, it is very suitable for minimizing resource usage and reducing idle resources that may occur during data migration. The choice between an active/active strategy using real-time data migration and active/passive strategy using snapshots, which is the assumed backup environment in our study, depends on several factors. Data migration supports the rapid recovery of complex systems, meeting complex situations and business continuity requirements. In contrast, the snapshot approach is more suitable for systems prioritizing cost, resource efficiency, and simplicity in the recovery process. To determine the optimal disaster recovery strategy, one must consider the system's characteristics, requirements, and cost-effectiveness. This decision-making process should also include technical indicators like the Recovery Time Objective (RTO). Therefore, for organizations aiming to minimize resource usage and maximize cost efficiency, the active/passive strategy, using our proposed method and module, can be an excellent alternative to the AWS or Google strategy. Avoiding complex data synchronization and migration while enabling quick and effective recovery when necessary is particularly suitable for scenarios prioritizing cost and operational efficiency. The AWS multi-region access point failover control is a high-availability feature that automatically switches data access requests to another AWS region within minutes. This ensures resilience and business continuity through multi-region architecture.