Interpretable Ai-based Resource Allocation For Virtual Machines In Cloud Platforms

Abstract

Cloud Computing Environments Require Efficient Storage Allocation Mechanisms To Handle The Rapid Growth Of Data While Maintaining Performance And Scalability. This Research Emphasizes Intelligent Storage Management As A Core Component By Integrating Virtualization With Dynamic Resource Allocation Strategies. Virtual Machines Are Utilized To Efficiently Distribute Storage And Computational Resources Across Physical Infrastructures, Ensuring Optimal Usage Of Available Capacity. Continuous Monitoring Of System Workloads Enables Adaptive Allocation Of Storage Resources, Reducing Redundancy And Preventing Inefficient Utilization. The Approach Enhances System Performance By Minimizing Storage Overhead And Ensuring Balanced Distribution Of Data Across The Cloud Environment. To Further Strengthen Storage Efficiency, A Time-To-Live (TTL) Based Data Self-destruction Mechanism Is Incorporated To Automatically Remove Expired Or Unnecessary Data. This Ensures That Storage Space Is Continuously Optimized Without Manual Intervention, Reducing Maintenance Complexity And Operational Costs. In Addition, Data Security Is Reinforced Through Blowfish Encryption Along With Secure Key Management Techniques Such As Key Rotation And Controlled Key Distribution. This Combination Of Intelligent Storage Allocation, Automated Data Lifecycle Management, And Strong Security Mechanisms Provides A Comprehensive Solution For Achieving Efficient, Secure, And Scalable Cloud Storage Systems.

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