Intelligent Backup Data Distribution Across Hybrid Storage Tiers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network-based backup solutions face challenges with high bandwidth consumption and latency during data restoration due to the need to transmit large amounts of data over remote storage locations, leading to increased costs and complexity.
Innovation Solution
The implementation of a hybrid backup architecture that utilizes virtual layering and machine learning to dynamically re-allocate backup data across storage locations, prioritizing locality for frequently accessed data and remote storage for less frequently accessed data, while monitoring access trends and storage health to optimize bandwidth and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backup data is stored at a remote network location, then data security and reliability are improved, but bandwidth consumption and latency during restoration increase
Solution Approach 1:
The backup data is segmented into multiple versions and distributed across different storage locations (local device, remote server, and peer devices). This segmentation allows the system to retrieve only necessary data segments during restoration, reducing bandwidth consumption while maintaining data security through distributed storage.
Solution Approach 2:
The system implements local quality by keeping frequently accessed backup versions locally or on nearby peers, while storing less frequently accessed versions remotely. This ensures that common restoration operations have low latency and bandwidth consumption, while maintaining the security benefit of remote storage for archival data.
2Reliability
If backup data is stored at a remote network location, then data security and reliability are improved, but restoration latency increases
Solution Approach 1:
The system performs preliminary actions by pre-caching frequently accessed backup versions on local devices and nearby peer devices before restoration is needed. This preliminary distribution ensures that when restoration is required, the data is already in optimal locations, reducing latency while maintaining the security architecture of distributed storage.
Solution Approach 2:
The backup architecture is dynamic, automatically adjusting where backup versions are stored based on access patterns, network conditions, and device availability. Frequently accessed data dynamically moves to local or peer devices to reduce latency, while less accessed data remains in remote storage for security, creating a flexible system that optimizes both speed and security.
3Reliability
If all backup versions are stored centrally, then data availability is improved, but storage costs and bandwidth consumption increase
Solution Approach 1:
The system merges multiple storage resources (local device storage, remote server storage, and peer device storage) into a unified backup architecture. This combination distributes the storage burden across multiple locations, reducing the storage capacity requirements at any single location while maintaining data availability through redundant copies across the distributed network.
Solution Approach 2:
Instead of storing all backup versions centrally, the system creates selective copies of backup data across multiple distributed locations. Peer devices store copies of backup versions they have or can provide, reducing the need for centralized storage capacity while ensuring data availability. This copying strategy leverages existing data across the network rather than duplicating everything centrally.
Data Source
AI summary
The claimed subject matter relates to systems and/or methodologies that facilitate intelligent distribution of backup information across storage locations in network-based backup architectures. A virtual layering of backup information across storage locations in the backup architecture can be implemented. Statistical models are utilized to dynamically re-allocate backup information among storage locations and/or layers to ensure availability of data, minimum latency upon restore, and minimum bandwidth utilization upon restore. In addition, heuristics or machine learning techniques can be applied to proactively detect failures or other changes in storage locations such that backup information can be reallocated accordingly prior to a failure.