Automated Storage Schema Generation for Enterprise Data
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Solution Overview
Problem
The manual selection and maintenance of storage components for data sets in complex computing scenarios are resource-intensive and inefficient, particularly as the number and variety of data sets and storage components increase, leading to suboptimal utilization of storage capacities and features, and making reevaluation challenging.
Innovation Solution
An automated process that identifies storage capabilities and data set factors to automatically select suitable storage components, provision space, and initiate storage, thereby generating a physical schema that conserves administrative resources and allows for efficient reconfiguration as data sets and storage components change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of storage components is performed, then administrators can make informed decisions about storage allocation, but the process consumes significant administrative time and resources
Solution Approach 1:
The system performs self-service by automatically evaluating storage components and data sets against predefined criteria and constraints, selecting optimal storage allocations without requiring continuous administrative intervention. The automated system serves itself by making informed decisions based on stored policies and current system state.
Solution Approach 2:
The manual mechanical process of administrator review and decision-making is replaced with an automated computational system that evaluates storage options algorithmically. The system substitutes human administrative actions with automated software processes that apply predefined criteria to select storage components.
2Reliability
If manual design of physical schema is performed, then administrators can optimize storage allocation, but the process becomes inefficient as the number and complexity of storage components increases
Solution Approach 1:
The physical schema design becomes dynamic and adaptive rather than static. The system continuously evaluates storage components and data sets, automatically adjusting allocations as conditions change. This dynamic approach allows the system to handle increasing complexity without proportionally increasing administrative burden.
Solution Approach 2:
An automated intermediary system is introduced between the storage components and data sets, acting as a mediator that applies optimization criteria and constraints to determine optimal allocations. This intermediary handles the complexity of matching storage resources with data requirements, improving efficiency as system scale increases.
3Reliability
If administrators manually maintain storage schemas, then they can ensure proper storage allocation, but reevaluation becomes challenging when data sets or storage components change
Solution Approach 1:
The system implements continuous feedback loops where storage allocations are automatically reevaluated when changes occur in data sets or storage components. The automated system monitors system state changes and triggers reevaluation processes, ensuring allocations remain optimal without requiring manual intervention for each change.
Solution Approach 2:
The system performs preliminary actions by pre-defining criteria and constraints for storage allocation before changes occur. When data sets or storage components change, the system has predetermined rules ready to apply, enabling rapid and reliable reconfiguration without ad hoc decision-making.
4Measurement precision
If comprehensive evaluation of storage components and data sets is performed, then optimal matching can be achieved, but the process requires significant administrative resources
Solution Approach 1:
The comprehensive evaluation process is segmented into discrete, automated steps that evaluate specific criteria independently. The system divides the complex matching problem into manageable segments such as evaluating storage capacity, performance characteristics, data set requirements, and constraints separately, then integrates results to determine optimal allocations.
Solution Approach 2:
The manual administrative process of evaluating and matching storage components with data sets is replaced with automated computational mechanisms. The system uses algorithms to perform comprehensive evaluations without requiring administrative time and energy, substituting human cognitive resources with automated processing capabilities.
Data Source
AI summary
An administrator of an enterprise storage set may be tasked with storing a large number and variety of data sets on a large number and variety of storage components. However, the manual selection of a physical schema by an administrator may be time-consuming, may generate inefficient physical schemata, and may not be easily reevaluated as the data sets and storage set change. Presented herein are techniques for automatically determining a physical schema by comparing the storage factors of each data set (e.g., data size, relationships with other data sets, and usages of the data set by users) with the storage capabilities of the storage components, selecting a suitable storage component, and implementing the storage of the data set on the storage component. An embodiment of these techniques may thereby achieve an automated identification of a physical schema with improved efficiency and flexibility of the physical schema while conserving administrative resources.