Hybrid Cloud Data Restoration Pattern Selection
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Solution Overview
Problem
Existing data restoration methods do not account for the complexities and costs associated with using public clouds as backup destinations, failing to provide an optimal restoration method that considers both restoration time and cost effectively.
Innovation Solution
A management method and apparatus that specifies restoration target data, selects appropriate restoration rules, and generates restoration patterns by reversing the copy source and destination roles based on backup configuration information to optimize data restoration in a hybrid cloud system, considering cost and time requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is restored from public cloud to on-premises, then data restoration is achieved, but restoration cost increases
Solution Approach 1:
The system dynamically determines restoration patterns by evaluating multiple factors including data size, restoration time requirements, and cost constraints. The management apparatus selects different restoration strategies (full restoration, incremental restoration, or selective restoration) based on real-time conditions, making the restoration process adaptive rather than static.
Solution Approach 2:
The system changes restoration parameters such as data transfer speed, compression ratio, and restoration priority levels to optimize the balance between cost and restoration effectiveness. By adjusting these parameters, the system can reduce restoration costs while maintaining acceptable restoration performance.
2Reliability
If data is restored from public cloud to on-premises, then data restoration is achieved, but restoration time increases
Solution Approach 1:
The system segments the restoration process into multiple independent tasks that can be executed in parallel. Large data restorations are divided into smaller chunks, allowing simultaneous transfer and processing operations, thereby reducing total restoration time while maintaining data integrity.
Solution Approach 2:
The system performs preliminary actions such as data compression, pre-processing, and preparation of restoration environments before actual data transfer begins. This preliminary work reduces the time required during the critical data restoration phase.
3Adaptability or versatility
If multiple restoration patterns are provided, then restoration flexibility improves, but system complexity increases
Solution Approach 1:
The management apparatus automatically selects appropriate restoration patterns based on predefined criteria and current system state, eliminating the need for manual intervention. The system self-manages the complexity of choosing among multiple restoration patterns by implementing automated decision-making logic that evaluates data characteristics, cost constraints, and time requirements.
Data Source
AI summary
A management apparatus including a storage device configured to store restoration rules which define a plurality of patterns relating to the restoration in a restoration configuration indicating a copy source and a copy destination of the data, and backup configuration information relating to the backup source and the backup destination of the data, the management apparatus executes: specification processing of specifying restoration target data; selection processing of selecting a specific restoration rule from the restoration rules; and generation processing of generating, by referring to the backup configuration information, in a restoration configuration of the restoration target data in which the backup source of the restoration target data specified by the specification processing is the copy destination and the backup destination of the restoration target data is the copy source, a restoration pattern of the restoration target data in accordance with the specific restoration rule selected by the selection processing.