Cloud Content Derivative Eviction Using Predictive Cost Modeling
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Solution Overview
Problem
Cloud-based content management systems face inefficiencies in managing storage costs due to the proliferation of both source and derivative content objects, with 'cold' objects being stored in high-performance appliances, leading to unnecessary expenses.
Innovation Solution
Applying a rule-based predictive model to identify candidate eviction objects and execute management commands to reduce storage costs while adhering to service level agreements, involving the use of a storage cost predictive model to rank and manage object evictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If additional high performance appliances are deployed to accommodate expanding content objects, then storage capacity increases, but storage costs increase
Solution Approach 1:
The system dynamically adjusts storage allocation by continuously monitoring access patterns and automatically transitioning content objects between hot and cold storage appliances based on their current activity status, ensuring storage capacity adapts to actual needs rather than static provisioning
Solution Approach 2:
The system changes the storage location parameter of content objects based on their access characteristics, moving frequently accessed objects to high performance appliances and infrequently accessed objects to lower cost appliances, thereby optimizing the cost-capacity tradeoff
2Speed
If cold content objects are stored in high performance appliances, then access speed is maintained, but storage costs increase unnecessarily
Solution Approach 1:
The system applies different storage quality characteristics to different content objects based on their access patterns, placing only hot content objects in high performance storage while moving cold content objects to lower cost storage, ensuring each object receives appropriate storage quality rather than uniform high performance storage
3Ease of operation
If derivative content objects are generated and stored for all source content objects, then content accessibility is improved, but storage capacity requirements increase
Solution Approach 1:
The system dynamically manages derivative content objects by generating them on-demand when source objects are accessed and removing them when no longer needed, rather than pre-generating and permanently storing all possible derivatives, thus reducing storage capacity requirements while maintaining accessibility
Data Source
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AI summary
Systems and methods for managing content in a cloud-based service platform. A method embodiment operates over storage content objects stored in storage devices in a cloud-based shared content management system. The method commences upon identifying a source object and identifying derivative objects that are generated based properties of the source object. After a time, candidate eviction objects are identified. One form of analysis is performed over source objects and another form of analysis is performed over derivative objects. Derivative objects are classified using the analysis, which classification is used to determine object management commands associated with the derivative object such as to remove the derivative object from one storage location (e.g., in a high-performance storage filer) and relocate it to another (e.g., lower cost) storage location. Based on the analysis, a derivative object might be deleted completely and then regenerated at a later time if/when needed.