Content-Aware Media Backup Storage Tiering
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Solution Overview
Problem
Current enterprise storage systems inefficiently manage and backup image and video media data due to lack of content-aware management, leading to wasteful resource usage and high costs, as they treat media data similarly to regular data without considering its unique characteristics and retention requirements.
Innovation Solution
A system and method for automatically evaluating and managing media data based on its content, applying transformations such as compression, format conversion, and segmentation, and storing it across different tiers of storage with varying retention periods, using algorithms like image processing and machine learning to assess significance and apply policies for optimal storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If media data files are backed up in lossless format like general data, then data integrity and compliance are maintained, but storage capacity and costs increase significantly
Solution Approach 1:
The patent applies parameter changes by transforming media data from lossless format to lossy compressed format, changing the quality parameter to reduce storage requirements. The system evaluates content significance and applies appropriate compression levels, transforming high-quality data into lower-quality representations that still meet retention requirements while reducing storage capacity needs.
Solution Approach 2:
The patent implements local quality by applying different compression and transformation levels to different portions of media data based on content significance. Critical content regions are preserved with higher quality while less important regions are compressed more aggressively, allowing selective maintenance of data integrity where needed while reducing overall storage requirements.
2Productivity
If media data files are stored in general purpose de-duplicated protection storage, then storage efficiency is improved through de-duplication, but media data characteristics and content significance are ignored leading to resource waste
Solution Approach 1:
The patent implements self-service by enabling media data to 'evaluate itself' through automated content analysis. The system uses image processing and machine learning algorithms to automatically assess content significance, determine retention requirements, and apply appropriate storage transformations without manual intervention, allowing the data to effectively manage its own storage lifecycle based on its characteristics.
Solution Approach 2:
The patent applies dynamics by making storage transformations adaptive and changeable over time. The system dynamically adjusts compression levels, format conversions, and retention periods based on evolving content significance assessments. Media data can transition between different storage tiers and transformation states as its perceived importance changes, rather than being statically assigned.
3Ease of operation
If arbitrary or standardized retention periods are set for media data backup, then backup management is simplified, but the time value and significance of media content cannot be accurately predicted or optimized
Solution Approach 1:
The patent implements feedback by continuously evaluating media data content significance and using that evaluation to adjust retention periods and storage transformations. The system monitors content characteristics, assesses their importance over time, and feeds this information back into storage policy decisions, automatically extending or shortening retention periods based on actual content value rather than fixed schedules.
Solution Approach 2:
The patent applies preliminary action by pre-establishing content evaluation algorithms and transformation libraries that enable automated assessment of media data significance. These evaluation mechanisms are prepared in advance to analyze content characteristics, predict retention requirements, and determine appropriate storage transformations before actual storage decisions are made, enabling proactive rather than reactive retention management.
Data Source
AI summary
The management and backup of image and video media is automatically performed by evaluating a media file to characterize the content; transforming the media file, as by compressing it, based upon its evaluation and established policies and requirements; and storing the transformed media file with a high resolution in a storage tier having first access characteristics for a first retention period. Following the first retention period, the stored file is re-evaluated and further transformed and stored with lesser resolution in another tier having different access characteristics for a second retention period. Subsequently, the further transformed media file may be transformed again and stored in archive storage.