Remote Desktop Gateway Resource Allocation via Neural Network Prediction
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Solution Overview
Problem
In virtualization, remote desktop gateways often face issues with insufficient or excessive resources, necessitating real-time monitoring and optimization to match current traffic demands and prevent resource waste.
Innovation Solution
A method involving a computer device that collects application data from virtual machines, trains a neural network using historical data to predict user logouts, and deploys virtual machines to designated remote desktop gateways based on real-time usage, adding or recycling gateways as needed to maintain optimal resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote desktop gateways are deployed to handle traffic demand, then service capacity is improved, but resource waste occurs when resources are excessive
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring usage metrics (CPU, memory, network) and automatically adjusting the number and configuration of remote desktop gateway instances. The system transitions from static deployment to dynamic scaling based on real-time traffic demand, ensuring service capacity matches actual needs without over-provisioning resources.
Solution Approach 2:
The system establishes a feedback loop where usage data from remote desktop gateways is collected, analyzed, and used to trigger automatic deployment or recycling actions. The classification model predicts future usage patterns and provides feedback to the deployment system, enabling proactive resource adjustment before resource waste or insufficiency occurs.
2Productivity
If remote desktop gateways are deployed to handle traffic demand, then service capacity is improved, but resource insufficiency occurs when resources are inadequate
Solution Approach 1:
The system performs preliminary actions by predicting future usage patterns using the trained classification model before resource insufficiency occurs. When the model predicts increased traffic demand, the system proactively deploys additional gateway instances in advance, ensuring service capacity is ready to handle upcoming demand without interruption or resource insufficiency.
Solution Approach 2:
The continuous monitoring and prediction feedback mechanism detects trends indicating upcoming traffic surges, triggering automated deployment actions before the system reaches resource insufficiency. This proactive feedback-driven approach ensures reliable service capacity by preparing resources ahead of demand spikes.
3Reliability
If virtual machines are deployed to remote desktop gateways, then service availability is improved, but resource allocation efficiency deteriorates without optimization
Solution Approach 1:
The system dynamically optimizes virtual machine placement on remote desktop gateway instances based on real-time resource utilization metrics. When gateways become underutilized, VMs are automatically migrated or recyled to consolidate resources, maintaining service availability while improving resource allocation efficiency by eliminating wasted capacity.
Solution Approach 2:
The system implements resource recycling by identifying and recyling underutilized or idle virtual machines and gateway instances. When traffic demand decreases, the system safely terminates excess VMs and recycles their resources for future use, ensuring resource allocation efficiency improves without compromising service availability during high-demand periods.
Data Source
AI summary
A method for deploying remote desktop gateways (RDGWs), a computer device, and a storage medium are provided. Application data of virtual machines (VMs) is obtained in RDGWs, the application data includes historical data and real-time data. A neural network is trained by using the historical data and a target classification model is obtained. The real-time data is inputted into the target classification model and a classification result of the real-time data is obtained. The VMs are deployed to a designated RDGW based on the classification result. The method allocates and deploys resources with greater efficiency.