Virtual Machine Pooling for Predictive On-Demand Allocation
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Solution Overview
Problem
Conventional digital platforms experience significant delays in creating and configuring virtual machines (VMs) for user access, leading to lengthy wait times, and there is a need for efficient VM management to reduce these delays and optimize VM usage based on user behavior.
Innovation Solution
A VM management platform that maintains a pool of VMs for on-demand access, utilizing data analytics and machine learning to analyze historical usage patterns, forecast demand, and dynamically allocate VMs based on user-specific requirements, reducing wait times and optimizing VM availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If VMs are created on-demand for each user request, then VM availability is improved, but user wait time increases significantly
Solution Approach 1:
The system performs preliminary actions by analyzing historical usage data to predict future VM requirements, and pre-configuring VM pools in advance based on these predictions. This allows VMs to be ready before users actually need them, eliminating wait times while maintaining availability.
Solution Approach 2:
The system dynamically adjusts VM pool sizes and configurations based on real-time usage patterns and predictions. It continuously monitors user behavior and automatically reconfigures the VM pool to match anticipated demand, ensuring both availability and minimal wait times.
2Speed
If a large pool of VMs is maintained for on-demand access, then user access speed is improved, but system cost increases
Solution Approach 1:
The system changes parameters of the VM pool dynamically - adjusting pool size, VM configurations, and resource allocation based on predicted usage patterns. This allows maintaining large pools only when needed while reducing costs during low-demand periods, achieving fast access without constant high overhead.
Solution Approach 2:
The system uses automated machine learning models to self-determine optimal pool configurations without manual intervention. It autonomously analyzes usage data, predicts requirements, and adjusts the VM pool accordingly, eliminating the need for over-provisioning while ensuring fast access when needed.
3Productivity
If VM pool configuration is manually optimized, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical optimization processes with automated machine learning algorithms. Instead of human operators manually analyzing and adjusting VM pool configurations, the system uses ML models to automatically analyze usage data and optimize resource allocation, maintaining high efficiency while reducing operational complexity.
Solution Approach 2:
The system implements continuous feedback loops where usage data is collected, analyzed by ML models, and used to automatically adjust VM pool configurations. This closed-loop system continuously improves resource efficiency based on actual performance data without requiring complex manual intervention or system reconfiguration.
Data Source
AI summary
Systems and method presented herein are configured to analyze pooling of virtual machines (VMs) to improve the functionality of such VMs. For example, a VM management platform is configured to maintain a pool of VMs including a plurality of VMs for on-demand access via a plurality of client devices; to access data relating to historical VM usage of VMs provided by the VM management platform; to analyze the data relating to the historical VM usage of the VMs provided by the VM management platform to identify one or more VM recommendations relating to one or more changes to the pool of VMs; and to implement the one or more changes to the pool of VMs based at least in part on the one or more VM recommendations.