Elastic Storage Volume Selection Engine for Cloud Optimization
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Solution Overview
Problem
Cloud storage environments often face inefficiencies due to over-provisioning or under-provisioning of resources, leading to suboptimal utilization of computing resources and inability to scale with growing storage demands, resulting in performance bottlenecks and increased costs.
Innovation Solution
An elastic volume type selection and optimization engine (eVSO engine) that dynamically assesses and adjusts block storage volume configurations based on multi-characteristics such as IOPS, throughput, and availability zones, recommending optimal volume types for real-time resource allocation and minimizing performance bottlenecks and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud resources are over-provisioned to ensure availability and performance, then reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic provisioning of block storage volumes by continuously monitoring workload characteristics and automatically adjusting volume type, size, and performance parameters. The system transitions from static over-provisioning to dynamic adaptation, where resources are allocated based on real-time demand patterns, thereby maintaining reliability while improving utilization efficiency.
Solution Approach 2:
The system changes multiple parameters simultaneously including volume type (e.g., from magnetic to SSD), volume size, and performance tiers based on analyzed workload characteristics. This multi-parameter optimization allows the system to match resource capabilities precisely to actual needs, resolving the contradiction between ensuring sufficient capacity and avoiding waste.
2Productivity
If cloud infrastructure is provisioned to meet current storage demands, then productivity is improved, but adaptability deteriorates when demands vary over time
Solution Approach 1:
The system performs preliminary analysis of workload characteristics and growth patterns to proactively provision appropriate storage capacity before demands fully materialize. By predicting future storage needs based on historical trends and workload analysis, the system maintains high productivity while being prepared for demand variations, thus improving both current performance and future adaptability.
Solution Approach 2:
The patent implements continuous monitoring and dynamic re-provisioning of storage volumes based on changing workload demands. The system automatically detects demand variations and adjusts volume configurations in real-time, enabling the infrastructure to adapt to varying storage requirements while maintaining optimal productivity levels throughout.
3Adaptability or versatility
If multiple storage volume types are provisioned to satisfy diverse workload requirements, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements self-service automation where the storage management engine automatically analyzes workload characteristics, selects appropriate volume types and configurations, and provisions resources without manual intervention. This automation handles the complexity of multi-type storage management internally, presenting a simplified interface to users while maintaining the adaptability benefits of diverse storage options.
Solution Approach 2:
The system dynamically changes storage parameters including volume type, size, and performance tier based on workload analysis. By automatically adjusting these parameters rather than requiring manual configuration of multiple fixed volume types, the system achieves workload compatibility while reducing operational complexity through centralized automated decision-making.
4Reliability
If storage resources are allocated based on peak demands, then reliability is improved, but loss of time increases due to under-utilization during low-demand periods
Solution Approach 1:
The patent implements dynamic resource allocation that continuously monitors actual storage utilization and adjusts volume provisioning accordingly. Instead of static peak-based allocation, the system adapts resource levels to match current demand, maintaining sufficient capacity for reliability while minimizing idle time during lower-demand periods through automated right-sizing operations.
Data Source
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AI summary
A system for elastic volume type selection and optimization is provided. The system may detect that a block storage volume was provisioned by a public cloud computing platform based on a first volume type identifier of a first volume type. The system may determine, based on a normalization model, a baseline operation rate and a baseline throughput rate for the provisioned block storage volume. The system may determine, based on a selected transition mode and historical performance measurements, a simulated operation rate and a simulated throughput rate. The system may communicate, in response to the simulated throughput being greater than the baseline throughput rate or the simulated operation rate being greater than the baseline operation rate, a provisioning instruction to re-provision the provisioned block storage volume on the cloud computing platform.