Dynamic Storage Pool Reconfiguration for Cloud Workloads
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Solution Overview
Problem
Current cloud computing environments face challenges in dynamically managing and optimizing resource utilization across storage pools, leading to inefficiencies due to statically configured storage pools and varying workload requirements.
Innovation Solution
Implement a system that monitors resource utilization across multiple storage pools, ranks them based on workload priority, and dynamically reconfigures resources by adding spare resources to over-utilized pools, moving resources between pools, combining complementary pools, or splitting pools based on workload priority.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If storage pools are statically configured and manually changed by system administrators, then configuration stability is maintained, but resource utilization efficiency deteriorates due to inability to dynamically adapt to varying workload demands
Solution Approach 1:
The patent implements dynamic resource allocation by automatically monitoring workload demands and reconfiguring storage pool configurations in real-time. The system transitions from static manual configuration to dynamic automated adjustment, allowing storage pools to adapt their capacity and performance characteristics based on actual workload requirements, thereby resolving the contradiction between configuration stability and dynamic adaptability
Solution Approach 2:
The system employs continuous monitoring of workload performance metrics and uses this feedback to automatically adjust storage pool configurations. By establishing a closed-loop control mechanism where performance data informs configuration changes, the system achieves both stability through systematic management and adaptability through responsive adjustments to changing conditions
2Ease of operation
If storage pools are manually configured by system administrators, then configuration control is maintained, but system complexity increases due to manual intervention requirements
Solution Approach 1:
The patent enables the storage system to self-manage its own configuration by implementing automated monitoring and self-adjustment capabilities. The system independently detects workload changes and reconfigures storage pools without requiring manual administrator intervention, thereby simplifying operation while maintaining configuration control through automated decision-making algorithms
Solution Approach 2:
The patent introduces an automated resource management intermediary layer that sits between the physical storage resources and the workloads. This intermediary automatically handles configuration decisions and resource allocation, reducing the complexity burden on system administrators while maintaining precise control over storage pool configurations through intelligent automation
3Productivity
If resources are dynamically reconfigured based on workload demands, then resource allocation efficiency is improved, but system stability may deteriorate due to frequent configuration changes
Solution Approach 1:
The patent implements controlled parameter changes by establishing threshold-based triggers for reconfiguration. Instead of continuously adjusting configurations, the system monitors workload parameters and only initiates changes when predefined thresholds are exceeded, thereby achieving efficient resource allocation while maintaining configuration stability through selective rather than constant modification
Solution Approach 2:
The patent employs preliminary analysis and planning before executing configuration changes. The system evaluates workload trends and predicts future demands, allowing it to make informed reconfiguration decisions that balance efficiency improvements with stability maintenance. By planning changes ahead and selecting optimal timing, the system minimizes disruption while achieving resource optimization
Data Source
AI summary
An approach for managing and optimizing available resources utilized in a networked computing environment (e.g., a cloud computing environment) is provided. In one aspect, a plurality of resources in a plurality of storage pools within the networked computing environment are monitored. Each storage pool is running at least one application workload using a corresponding subset of the plurality of resources. The storage pools are ranked based on the application workloads. In response to an over-utilized storage pool and/or an under-utilized storage pool, the plurality of resources are dynamically reconfigured.