Machine Learning Cloud Storage Management for Cost Control
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Solution Overview
Problem
Current cloud storage management solutions lack the necessary insights, flexibility, and control to effectively address inefficiencies such as over-provisioning, underutilization, and hidden creeping costs, leading to significant financial and operational inefficiencies and environmental impact.
Innovation Solution
A method and system utilizing machine learning models to analyze cloud account information, classify storage resources, determine utilization parameters, and generate commands to manage cloud storage resources efficiently, including AI-based budget estimation and threshold controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud storage resources are over-provisioned to ensure availability and scalability, then service reliability and accessibility are improved, but operational costs and resource waste increase
Solution Approach 1:
The system performs preliminary classification of storage resources into categories (hot, warm, cold, archive) based on historical access patterns and metadata analysis. This advance categorization enables proactive allocation strategies where resources are pre-positioned in appropriate storage tiers before actual access occurs, ensuring availability for frequently accessed data while minimizing costs for rarely accessed data.
Solution Approach 2:
The system implements dynamic resource allocation that continuously monitors access patterns and automatically adjusts storage tier assignments. Resources are not statically over-provisioned but dynamically shifted between storage tiers based on real-time and historical usage data, allowing the system to maintain reliability for active resources while reducing operational costs for inactive resources.
2Ease of operation
If cloud storage resources are allocated with a set-and-forget approach, then ease of operation is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system implements self-service automation where machine learning models automatically classify storage resources, determine optimal tiers, and execute allocation decisions without human intervention. The system monitors its own performance, detects underutilized resources, and autonomously reassigns them to appropriate tiers, maintaining operational simplicity while dramatically improving resource utilization efficiency through intelligent automation.
3Device complexity
If traditional cloud storage management solutions are used, then device complexity is reduced, but the ability to detect and measure storage needs and growth rates deteriorates
Solution Approach 1:
The system introduces an intermediary intelligent layer between users and cloud storage resources. This layer includes machine learning models and analytics engines that automatically analyze storage patterns, predict future needs, and generate actionable insights. Users interact with this simplified intermediary through intuitive interfaces rather than directly managing complex storage configurations, maintaining ease of use while enabling sophisticated detection and measurement of storage needs.
Data Source
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AI summary
A method for facilitating managing cloud storage for operations includes obtaining account information associated with a cloud account, analyzing the account information using a machine learning model, obtaining storage data corresponding to categories from the account information based on the analyzing, determining a value for a parameter associated with a utilization of the cloud storage resource based on the storage data, determining an action required to be implemented for the cloud account based on the determining, generating a cloud account controlling command for implementing the action for the cloud account based on the action, transmitting the cloud account controlling command to a cloud platform device, and storing the account information.