Predictive Cloud Storage Capacity Adjustment
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Solution Overview
Problem
Current cloud-based storage systems lack adaptive provisioning capabilities, leading to inefficiencies such as over-provisioning, poor resource utilization, and increased costs due to the inability to automatically adjust storage capacity according to changing application needs.
Innovation Solution
A predictive model, potentially using machine learning techniques like neural networks or reinforcement learning, is employed to identify future times when storage capacity adjustments are required, allowing for the dynamic addition or removal of block storage capacity from cloud providers, optimizing resource utilization and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If storage over-provisioning is used to minimize the risk of data loss or crashes, then reliability is improved, but resource utilization deteriorates and costs increase
Solution Approach 1:
The patent implements dynamic storage provisioning that automatically adjusts storage capacity based on real-time application needs and historical usage patterns. The system transitions from static over-provisioning to dynamic allocation, where storage volumes are automatically scaled up or down to match actual demand, thereby maintaining reliability while optimizing resource utilization and reducing waste.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring storage usage metrics, application performance data, and access patterns. This feedback loop enables the automated storage management system to make informed decisions about when to provision or de-provision storage capacity, ensuring that storage levels align with actual needs rather than static estimates.
2Reliability
If manual monitoring and optimization cycles are performed to ensure stability and performance, then reliability is improved, but productivity deteriorates due to constant human intervention
Solution Approach 1:
The patent implements a self-service automated storage management system that autonomously monitors storage usage, predicts future needs, and adjusts provisioning without human intervention. The system uses machine learning models to analyze historical data and automatically makes provisioning decisions, eliminating the need for manual monitoring cycles while maintaining system stability and performance.
Solution Approach 2:
The system replaces manual mechanical processes (human engineers performing monitoring and optimization) with automated computational processes. Machine learning algorithms and automated decision-making systems substitute for human intervention, enabling continuous optimization without the productivity loss associated with manual cycles.
3Device complexity
If cloud block storage does not automatically adjust to changing application needs, then device complexity is reduced, but adaptability deteriorates leading to application crashes and data loss
Solution Approach 1:
The system performs preliminary actions by predicting future storage needs before actual demand occurs. Using historical usage patterns and machine learning models, the system provisions storage capacity in advance of predicted requirements, preventing application crashes and data loss while maintaining simple management interfaces for users.
Data Source
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AI summary
Systems and methods for managing computer block storage for a computer application include calculating an optimal required block storage capacity based on the storage needs of the application; provisioning block storage of the optimal capacity; receiving at least one block storage usage metric of the application; using a machine learning based model, trained on historic data of at least one application, to identify at least one future time at which a block storage capacity adjustment is required; and adjusting the block storage capacity within a time of the future time at which the block storage capacity adjustment is required.