Local cluster high availability based on shared storage and remote data disaster recovery
RoseHADR is a solution providing local cluster high availability and data remote disaster recovery. To build RoseHADR solution, user just need to add one remote data disaster recovery server to existing local RoseHA cluster. Then it can realize the remote disaster recovery protection for kernel data and provide a stable, efficient and reliable solution for enterprises.
Based on high availability cluster architecture based on shared storage, the standby server is in always standby mode. In case of any fault, it can timely takeover related business, to ensure the business continuity.
It will monitor the system resources like CPU/memory/ hard disk/ network and so on in real time, and support intelligent alarm and failover.
It will capture the production data I/O in high availability cluster in real time, and replicate these data through TCP/IP network to standby node. Then a real-time copy data and snapshot data will be stored on standby node.
It will regularly generate data snapshot file. History copy data by hour, day, week, month and year will be retained.
To prevent data loss risk caused by different disaster scenarios, such as HW fault, SW fault, virus infection, man-made error and so on.
The disaster recovery data can be recovered to original production server or any other 3-party server.
The granularity of data to be recovered is user-defined. Both the latest real-time data and history snapshot data can be applied to do data recovery. Besides, user can flexibly select file, directory or whole backup dataset to recover.
The brand new Web management view supports the mainstream browsers, so user can conveniently execute cross-platform management in a centralized way.
It supports diverse alert mechanism, so that administrator can timely handle the alerted fault or error.
The cluster-level disaster recovery guarantees protection for both business and data, satisfying the requirement of Cybersecurity Classified Protection Standards 2.0.