Implicit Object Mapping for Storage Management
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Solution Overview
Problem
Current storage systems face challenges in managing stored objects based on associated applications due to communication and processing overhead, lack of APIs, and the need for explicit mapping, which limits scalability and efficiency in associating objects with storage management objects.
Innovation Solution
The system infers relationships between stored objects and storage management objects using attributes like location, size, type, and access patterns, and applies machine learning to determine implicit mappings, enabling automatic association and policy enforcement without explicit user configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit mapping configuration is used to associate stored objects with storage management objects, then mapping accuracy is improved, but communication overhead and processing time increase
Solution Approach 1:
The storage system performs self-service by automatically inferring the mapping between stored objects and storage management objects using machine learning models. The system analyzes metadata, access patterns, and object attributes to determine associations without requiring external queries to application servers, thereby eliminating communication overhead while maintaining accurate mapping through automated analysis.
Solution Approach 2:
The patent replaces the mechanical communication-based mapping system with an intelligent inference system. Instead of querying application servers to determine object associations, the system uses machine learning models that analyze object attributes and patterns to infer mappings, substituting physical communication processes with computational intelligence.
2Measurement precision
If explicit mapping configuration is used to associate stored objects with storage management objects, then mapping accuracy is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models offline using historical data. The models are prepared in advance to quickly infer mappings during runtime, shifting the computational burden from online processing to offline preparation. This reduces real-time resource consumption while maintaining accurate mapping capabilities.
Solution Approach 2:
The patent replaces resource-intensive explicit configuration processes with efficient machine learning inference. The system substitutes manual or query-based mapping determination with pre-trained models that rapidly analyze object attributes and generate mappings with minimal computational overhead during operation.
3Adaptability or versatility
If automatic inference is used to associate stored objects with storage management objects, then scalability is improved, but mapping precision may deteriorate
Solution Approach 1:
The system implements dynamics by making the mapping inference process adaptive and learnable. The machine learning models continuously improve their accuracy by learning from historical data and patterns, allowing the system to scale while maintaining or improving mapping precision over time. The inference process adapts to different workloads and storage patterns automatically.
Solution Approach 2:
The system incorporates feedback mechanisms where the accuracy of inferred mappings is evaluated and used to improve the machine learning models. By analyzing the results of automatic inference and comparing them with actual usage patterns, the system refines its models to maintain high precision while scaling to handle larger numbers of stored objects and diverse workloads.
Data Source
AI summary
The present application discloses a method, system, and computer system for associating stored objects with a storage management object. The method includes inferring, based at least in part on metadata comprising or otherwise associated with a set of stored objects stored in the memory or other data storage device, an interdependence among the stored objects comprising the set of stored objects, associating the stored objects comprising the set of stored objects with a storage management object, and applying a storage management policy to the stored objects comprising the set of stored objects based at least in part on said association of the stored objects comprising the set of stored objects with the storage management object.