Dynamic Data Storage Placement Using Segmentation and Local Quality
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Solution Overview
Problem
In large-scale computing and data storage systems, determining the optimal location and method for storing data is challenging due to varying costs, retrieval speeds, regulatory requirements, and commercial expectations, which can lead to issues like bandwidth limitations and uneven load distribution across storage devices.
Innovation Solution
A component, such as a storage choice component, uses a rules engine to determine the best storage location and method based on factors like cost, retrieval speed, encryption level, geographic location, and usage patterns, and a migration engine to adjust storage over time, potentially employing machine learning to optimize data placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If data is stored in geographically distant locations, then storage capacity and distribution are improved, but bandwidth availability and retrieval speed deteriorate
Solution Approach 1:
The patent segments data into multiple copies or fragments and distributes them across different storage locations. This allows the system to maintain high storage capacity through geographic distribution while enabling faster retrieval by accessing only the necessary segments from nearby locations, rather than transferring entire datasets from distant storage facilities.
Solution Approach 2:
The patent implements local quality by placing frequently accessed data or critical data segments in geographically closer storage locations with higher bandwidth availability, while less frequently accessed data can be stored in more distant locations. This creates a hierarchical storage architecture where retrieval speed is optimized for local operations while maintaining overall storage capacity.
2Volume of stationary object
If many storage devices are used, then storage capacity and redundancy are improved, but load balancing and system complexity worsen
Solution Approach 1:
The patent creates a universal storage system where multiple storage devices can be dynamically assigned to different functions based on system needs. The same pool of storage devices can serve as primary storage, backup storage, or archive storage depending on data characteristics and access patterns, reducing the need for specialized devices for each function and simplifying overall system management.
Solution Approach 2:
The patent implements self-service through automated load balancing and data placement algorithms that dynamically manage data across storage devices without manual intervention. The system automatically monitors device utilization, retrieves performance metrics, and redistributes data to maintain optimal load balancing, thereby reducing operational complexity despite having many storage devices.
3Reliability
If data is stored with high security measures, then data protection is improved, but storage cost and retrieval time worsen
Solution Approach 1:
The patent applies dynamic security measures where the level of encryption and security protocols applied to data changes based on access patterns, data sensitivity, and user credentials. Frequently accessed data may use lighter encryption to reduce retrieval overhead, while sensitive or infrequently accessed data receives stronger protection. This dynamic approach maintains high data protection while minimizing retrieval time penalties.
Solution Approach 2:
The patent implements preliminary action by pre-decrypting or pre-processing data that is anticipated to be accessed frequently, storing it in a more accessible format while maintaining security through controlled access mechanisms. This allows the system to maintain strong security protocols while reducing the computational overhead and retrieval time when authorized users need access to the data.
Data Source
AI summary
Data may be stored in a location or manner that takes various considerations into account. Examples of such considerations are the availability, speed and cost of storage resources, and commercial and regulatory expectations concerning the reliability, security, and/or availability of the stored data. When a piece of data is to be stored, a storage choice component may take the above-considerations into account in order to determine where the data is to be stored, how many copies of the data are to be made, whether the data is to be encrypted, and/or other issues. Additionally, a migration engine may re-evaluate data that has already been stored in order to determine whether the data may be migrated to other storage resources, and/or whether changes may be made with regard to issues such as the encryption level and/or the number of stored copies of the data.