Cloud Block Storage Optimization via Account Merging
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Solution Overview
Problem
Current cloud block storage systems face inefficiencies in resource allocation and data replication costs due to the need for multiple storage accounts across different regions, leading to increased overhead and latency in data processing and retrieval.
Innovation Solution
A system that optimizes block storage volumes by merging multiple storage accounts and re-provisioning resources based on historical utilization metrics and predictive analysis, using a block storage volume optimization (BSO) stack to generate tokens for merging storage accounts and recommending resource configurations that reduce data replication costs and enhance processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple storage accounts are maintained across different regions, then data redundancy and availability are improved, but data replication costs and system complexity increase
Solution Approach 1:
The patent merges multiple storage accounts into a unified storage account structure, consolidating data redundancy management while maintaining cross-region availability. This reduces the number of independent accounts from multiple to one, simplifying management overhead while preserving reliability through the unified account's ability to manage replicated data across regions.
Solution Approach 2:
The unified storage account serves multiple functions simultaneously: it manages data redundancy, handles cross-region replication, and provides a single point of control for multiple block storage volumes. This multi-functional approach replaces the need for separate specialized accounts for each function, reducing overall system complexity.
2Reliability
If multiple storage accounts are maintained across different regions, then data redundancy is improved, but data replication costs increase
Solution Approach 1:
By merging storage accounts, the system consolidates data redundancy management into a single account, reducing duplicate replication operations. The unified account can optimize replication paths and avoid redundant data copying that would occur with multiple independent accounts, thereby reducing replication costs while maintaining redundancy.
Solution Approach 2:
The system uses virtualization and token-based access (BSO tokens) to provide copy-like access to data across regions without requiring physical duplication of entire storage accounts. This allows data redundancy through virtual access rather than expensive physical replication of all data.
3Productivity
If storage accounts are merged, then resource allocation efficiency is improved, but storage capacity constraints may be exceeded
Solution Approach 1:
The system dynamically adjusts resource allocation within the unified storage account based on actual usage patterns and demands. The BSO stack continuously monitors resource utilization and reconfigures allocation in real-time, allowing the system to handle variable storage capacity requirements while maintaining high allocation efficiency. This dynamic approach enables the merged account to adapt to capacity constraints rather than being limited by fixed allocations.
Data Source
AI summary
A system includes a multi-layer block storage volume optimization (BSO) stack to generate a BSO token containing prescriptions to optimize block storage volume. The system may receive account information of storage accounts associated with block storage volumes; obtain respective storage regions and respective data redundancy types of the first storage account and the second storage account from the first account information; and generate the BSO token to include instructions to merge the storage accounts according to the respective storage regions and the respective data redundancy types. The system may further obtain historical resource utilization data for the block storage volume; generate historical resource utilization metrics from the historical resource utilization data; determine a resource configuration baseline based on the historical resource utilization metrics; and select a recommended resource configuration from a plurality of resource configurations based on the first resource configuration baseline.