Cloud Block Storage Volume Account Merging for Cost Control
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Solution Overview
Problem
Existing cloud block storage systems face inefficiencies in resource allocation and utilization, leading to increased costs and suboptimal performance due to unmanaged and uncoordinated use of storage accounts and volumes.
Innovation Solution
A system for optimizing block storage volumes by merging storage accounts and rightsizing resource configurations, including storage capacity, operation rate, and throughput rate, using a multi-layered block storage volume optimization stack that performs prescriptive analysis and generates optimization recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple storage accounts are used to manage block storage volumes, then storage capacity and flexibility are increased, but resource allocation efficiency and cost increase due to uncoordinated management
Solution Approach 1:
The system merges multiple storage accounts into a unified managed storage account, consolidating resource management while preserving the underlying storage capacity. This allows coordinated optimization of IOPS, throughput, and storage capacity across previously separate accounts, resolving the contradiction between having multiple accounts for capacity/flexibility and the inefficiency of uncoordinated management.
2Reliability
If storage capacity, operation rate, and throughput rate are over-provisioned to ensure performance, then data retrieval speeds and reliability are improved, but costs increase due to unused resources
Solution Approach 1:
The system dynamically adjusts storage capacity, IOPS, and throughput allocations based on real-time workload demands and historical usage patterns. This allows the system to maintain reliable data retrieval performance when needed while automatically scaling down resource allocation during low-demand periods, eliminating the need for static over-provisioning and reducing wasted costs.
Solution Approach 2:
The system continuously optimizes key parameters including storage capacity, IOPS, and throughput rates based on actual usage metrics. By monitoring and adjusting these parameters dynamically, the system ensures reliable performance while preventing cost waste from fixed over-provisioned configurations.
3Productivity
If storage accounts are merged to optimize resource allocation, then cost and efficiency are improved, but data replication and transfer operations increase during the merging process
Solution Approach 1:
The system performs preliminary analysis and planning before executing storage account merges, identifying optimal merge sequences and potential data redundancy. By preparing optimization strategies in advance and detecting duplicate or redundant data beforehand, the system minimizes unnecessary data replication and transfer operations during the actual merging process, reducing the associated costs and energy consumption.
Data Source
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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.