Cloud Object Defragmentation with Read-Only Attribute Management
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Solution Overview
Problem
Primary storage systems' robust features like data replication, deduplication, encryption, and snapshot management are incompatible with cloud storage environments, leading to loss of functionality when used in hybrid setups, and existing solutions fail to provide efficient space and resource management, scalable analytics, and cost-effective storage solutions.
Innovation Solution
A system that maintains cloud objects with a read-only attribute, allowing modification of unused data through defragmentation and garbage collection without affecting in-use data, and enables pseudo read-only snapshots with additional metadata for analytics and virus scans, while ensuring data consistency and deduplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If primary storage systems are used for robust data management features, then data replication, deduplication, encryption, and snapshot management are improved, but compatibility with cloud storage environments deteriorates and functionality is lost
Solution Approach 1:
The system segments storage functionality into two parts: primary storage systems handle robust data management features (replication, deduplication, encryption, snapshots), while cloud storage environments handle scalable analytics and cost-effective storage. This segmentation allows each component to operate in its optimal environment without compatibility conflicts.
Solution Approach 2:
The patent introduces an intermediary layer that manages data between primary storage and cloud storage, enabling features like cloud-based defragmentation and garbage collection. This intermediary allows robust primary storage features to work with cloud storage environments by translating and coordinating operations across both systems.
2Quantity of substance
If cloud storage is used for scalable and cost-effective storage, then storage cost and scalability are improved, but robust data management features deteriorate
Solution Approach 1:
The system divides storage responsibilities: cloud storage provides scalable capacity and cost-effective storage, while primary storage systems provide robust data management features. This segmentation allows the organization to achieve both scalability and functional reliability.
Solution Approach 2:
The patent implements multi-functionality by enabling cloud storage to perform multiple roles: cost-effective storage, scalable analytics processing, and hosting data management operations like defragmentation and garbage collection that were traditionally only available in primary storage systems.
3Productivity
If defragmentation is performed on cloud objects, then storage efficiency is improved, but data consistency and read-only attributes may deteriorate
Solution Approach 1:
The system dynamically adjusts object attributes based on operation type. Cloud objects have read-only attributes during normal operations to ensure consistency, but temporarily transition to writable state during defragmentation operations. This dynamic attribute management allows storage efficiency improvements while maintaining data consistency.
Solution Approach 2:
The patent performs preliminary actions by creating snapshots or copies of cloud objects before defragmentation. This preliminary copying ensures that the original data remains consistent and unchanged, while the copy undergoes defragmentation to improve storage efficiency.
4Adaptability or versatility
If additional metadata is added to cloud objects for analytics, then analytics capability is improved, but object complexity and processing overhead deteriorate
Solution Approach 1:
The patent adds analytics capability by introducing a new dimension - metadata layers attached to cloud objects. This metadata provides analytics information without fundamentally changing the core object structure, allowing analytics processing while maintaining object simplicity.
Solution Approach 2:
The system uses metadata as an intermediary layer between the simple cloud objects and complex analytics requirements. This metadata layer provides the necessary information for analytics without requiring complex changes to the underlying object structure or processing logic.
Data Source
AI summary
Techniques are provided for managing objects within an object store. An object is maintained within an object store. The object comprises a plurality of slots. Each slot is used to store a unit of data accessible to applications hosted by remote computing devices. The object comprises an object header used to store metadata for each slot. A determination is made that the object is a fragmented object comprising an in-use slot of in-use data and a freed slot from which data was freed. The object is compacted to retain in-use data and exclude freed data as a rewritten object.