File-System Metadata Grouping for Lower-Cost Cloud Object Storage
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Solution Overview
Problem
Cloud object storage for unstructured data is costly due to high frequency of read and write operations and inefficient allocation of disk space, particularly in data backup and archiving scenarios.
Innovation Solution
Optimize cloud object storage by intelligently grouping data based on file attribute similarities and storing it as dedicated or shared objects, using file-system metadata to determine the optimal object size for efficient storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud object storage is used for unstructured data backup, then data can be stored elastically and scalably, but storage costs increase due to high frequency of read and write operations
Solution Approach 1:
The patent merges multiple files into a single shared object when they share common metadata attributes (such as same parent directory, creation time, or other file system metadata). This consolidation reduces the total number of objects stored in the cloud, thereby decreasing the frequency of read and write operations and lowering storage costs while maintaining the elastic scalability of cloud object storage
2Adaptability or versatility
If cloud object storage is used for unstructured data backup, then data can be stored elastically and scalably, but disk space allocation becomes inefficient
Solution Approach 1:
The patent combines multiple files with similar metadata characteristics into shared objects, which optimizes disk space allocation by eliminating redundancy. When files share common metadata, they are grouped together in shared objects, allowing more efficient use of disk space compared to storing each file as a separate object
Solution Approach 2:
The patent creates shared objects that can serve multiple files simultaneously, making a single object serve multiple purposes. This multi-functionality approach allows the same object to represent multiple files, improving disk space efficiency while maintaining the ability to store and retrieve any individual file when needed
Data Source
AI summary
A method and system for leveraging file-system metadata for direct to cloud object storage optimization. Under cloud object storage architecture, any unstructured data may be managed and stored in the cloud as objects. Objects thus provide an elastic, scalable format through which unstructured data may be maintained for a variety of purposes, including those directed to data backup, archiving, and/or disaster recovery. Cloud object storage, however, tends to be costly—mainly stemming from factors, such as the frequency of read and write operations (also referred to as runtime costs) applied to, as well as the allocation of disk space (also referred to as storage costs) consumed by, any number of objects configured to store file backup copies. In addressing at least the aforementioned, embodiments disclosed herein optimize cloud object storage utilization through the intelligent grouping and object storing of data based on file attribute similarities.