Cloud Archival Storage Data Restoration via Segmented Access
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Solution Overview
Problem
Restoring data from cloud archival storage tiers incurs additional network, computing, and monetary costs when exceeding storage thresholds in hot storage tiers, due to the need to change storage classes and reconstruct tree data structures, which can be time-consuming and costly.
Innovation Solution
A system that uses an index-based approach to segment data requests into portions, allowing for efficient storage class changes and data restoration from cloud archival storage to hot storage without exceeding storage thresholds, utilizing a walker mechanism to monitor and optimize the restoration process, and reconstructing tree data structures for operable file restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is restored from cloud archival storage to hot storage, then data accessibility and speed are improved, but additional network, computing, and monetary costs are incurred when exceeding storage thresholds
Solution Approach 1:
The patent segments large data restoration requests into smaller portions that fit within hot storage tier thresholds. The system divides the archival data into multiple chunks, restores them sequentially to hot storage, and deletes them after successful restoration to avoid threshold violations and associated costs.
Solution Approach 2:
The system performs preliminary actions by pre-calculating the size of data to be restored and comparing it against hot storage thresholds before initiating the restoration process. This allows the system to plan the restoration strategy in advance, ensuring cost-effective execution.
2Loss of time
If storage class is changed from cold storage tier to hot storage tier, then data restoration time is reduced, but additional monetary costs are incurred
Solution Approach 1:
The patent segments the data restoration process into multiple phases, changing storage class for only the necessary portions of data at any given time. By dividing the archival data into smaller units, the system can efficiently transition storage classes without incurring costs for the entire dataset.
Solution Approach 2:
The system dynamically changes the storage class parameter for specific data portions based on restoration needs. It monitors hot storage tier utilization and adjusts the storage class of archival data segments accordingly, transitioning from cold to hot storage only when necessary and beneficial.
3Productivity
If hot storage tier is used for frequently accessed data, then performance is improved, but storage threshold limits the amount of data that can be stored
Solution Approach 1:
The patent applies segmentation by dividing archival data into smaller segments that can fit within hot storage tier capacity limits. The system restores these segments on-demand to hot storage, ensuring high performance for accessed data while maintaining cost efficiency by not permanently storing large volumes in the expensive tier.
Solution Approach 2:
The system dynamically adjusts the amount of data in hot storage based on access patterns and current thresholds. Hot storage capacity is treated as a dynamic resource that expands and contracts based on actual restoration needs, rather than a fixed allocation.
Data Source
AI summary
A request for one or more files is received. It is determined that the requested one or more files have been archived at an archival storage tier. Access to a first portion of the one or more requested files stored at the archival storage tier is requested. Access is provided to the first portion in response to the request. Based at least in part on a metric associated with a utilization of the first portion, it is determined when to request access to a second portion of the one or more requested files stored at the archival storage tier.