Hierarchical Storage Management for Converged Systems
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Solution Overview
Problem
Current converged systems fail to effectively manage storage resources, compute resources, and do not hierarchically allocate or use data storage resources, lacking sufficient management functionality for storage capacity and data volumes, as well as not implementing storage layer specialization.
Innovation Solution
A method that identifies storage resources, creates storage capacity with associated attributes, defines data volumes inheriting these attributes, and configures volume shares with additional attributes, enabling hierarchical management and specialization of storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current converged systems are used to manage resources, then resource pooling is enabled, but storage resource management functionality is insufficient
Solution Approach 1:
The patent segments storage resource management into three hierarchical levels: storage capacity (pool-level), data volumes (volume-level), and volume shares (share-level). Each level has its own attributes and management functions, allowing detailed control at each granularity while maintaining overall system coordination through the convergence manager.
Solution Approach 2:
The patent introduces a hierarchical dimension to storage resource management by creating multiple levels of abstraction (capacity → volumes → shares). This dimensional expansion enables simultaneous management of aggregate storage pools and individual access permissions, resolving the contradiction between high-level pooling and detailed control.
2Adaptability or versatility
If hierarchical allocation of storage resources is not implemented, then system simplicity is maintained, but storage resources cannot be hierarchically managed and allocated
Solution Approach 1:
The patent implements a nested hierarchical structure where volume shares are contained within data volumes, which are in turn contained within storage capacities. This nesting enables hierarchical allocation where parent entities can be managed independently while child entities inherit and refine attributes, achieving complex allocation without proportional complexity increase.
Solution Approach 2:
The system performs preliminary actions by pre-defining storage capacities with attributes before creating data volumes and volume shares. This advance structuring enables automatic inheritance and simplifies subsequent allocation operations, as the hierarchical framework is already in place to guide resource distribution.
3Adaptability or versatility
If storage layers are not specialized, then system simplicity is maintained, but storage layer specialization cannot be implemented
Solution Approach 1:
The patent applies local quality by allowing different storage layers (capacity, volume, share) to have specialized attributes and functions appropriate to their level. Each layer can be optimized independently with specific properties (e.g., capacity has total size, volumes have access controls, shares have mount paths), enabling specialization without requiring complete architectural redesign.
Data Source
AI summary
A computer-implemented method according to one embodiment includes identifying a plurality of storage resources. Additionally, the method includes creating a storage capacity, where the storage capacity has a first plurality of associated attributes. Further, the method includes defining one or more data volumes for the storage capacity, where each of the one or more data volumes has a second plurality of associated attributes and inherits the first plurality of associated attributes. Further still, the method includes configuring one or more volume shares for each data volume, where each of the volume shares has a third plurality of associated attributes and inherits the first plurality of associated attributes as well as the second plurality of associated attributes.