Host Driver Event Journaling for Real-Time Data Protection
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Solution Overview
Problem
Current data protection and recovery solutions are inefficient and costly due to their inability to keep pace with rapid data growth and real-time requirements, leading to high costs, risks, and productivity losses in enterprise environments.
Innovation Solution
A data management system that provides continuous, real-time data protection and recovery services across networks using commodity processors, with a host driver that captures and translates data transactions into application-aware event journals, enabling efficient data services such as protection, disaster recovery, and replication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional schedule based tape and in-storage data protection approaches are used, then data protection is provided, but they cannot keep pace with rapid data growth and real-time requirements
Solution Approach 1:
The system transitions from static, schedule-based data protection to dynamic, continuous event journaling that adapts to real-time data changes. The host driver continuously captures data events as they occur, enabling the system to keep pace with rapid data growth while maintaining data availability through real-time protection.
Solution Approach 2:
The system performs preliminary action by continuously journaling data events in real-time before disasters or failures occur. This continuous preparation ensures that data is already protected and ready for rapid recovery, eliminating the need to wait for scheduled backup operations.
2Reliability
If redundancy of infrastructure is focused, then data protection capability is improved, but without data availability, applications cannot be available
Solution Approach 1:
The host driver acts as an intermediary between applications and the data management system, capturing data events at the source and continuously journaling them. This intermediary approach ensures both data protection capability and application availability by protecting data in real-time without disrupting application operations.
Solution Approach 2:
The system enables self-service by continuously protecting data automatically without requiring manual intervention or application downtime. The continuous event journaling operates autonomously, ensuring data protection while maintaining application availability and productivity.
3Adaptability or versatility
If legacy data protection solutions are fragmented across multiple applications and systems, then comprehensive coverage is achieved, but overhead and data management complexity increase
Solution Approach 1:
The host driver provides universal, multi-functional data protection by continuously journaling all data events from multiple applications and systems through a single unified interface. This eliminates the need for fragmented protection solutions while reducing management complexity through centralized event capture and processing.
Solution Approach 2:
The system merges fragmented data protection functions into a unified continuous event journaling approach. By combining multiple protection mechanisms into a single continuous journaling process, the system achieves comprehensive coverage while simplifying data management and reducing overhead.
4Reliability
If manual recovery techniques are used, then data recovery is performed, but human errors and process issues constitute 80% of unplanned downtime
Solution Approach 1:
The system enables self-service recovery by continuously maintaining event journals that automatically capture all data changes. In the event of failure, the system can automatically restore data from these journals without manual intervention, eliminating human errors and reducing unplanned downtime caused by manual recovery processes.
Solution Approach 2:
The continuous event journaling provides real-time feedback on data changes, enabling automatic detection and recovery from failures. This feedback mechanism ensures that data recovery can be performed rapidly and accurately without relying on manual processes that introduce errors and delays.
Data Source
AI summary
A data management system or “DMS” provides a wide range of data services to data sources associated with a set of application host servers. The data management system typically comprises one or more regions, with each region having one or more clusters. A given cluster has one or more nodes that share storage. To facilitate the data service, a host driver embedded in an application server connects an application and its data to a cluster. The host driver provides a method and apparatus for capturing real-time data transactions in the form of an event journal that is provided to the data management system. The driver functions to translate traditional file/database/block I/O into a continuous, application-aware, output data stream. Using the streams generated in this manner, the DMS offers a wide range of data services that include, by way of example only: data protection (and recovery), disaster recovery (data distribution and data replication), data copy, and data query and access.