Predicting Virtual Machine Resource Consumption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Hypervisors or virtual machine monitors struggle to accurately predict resource allocation needs for virtual machines providing remote desktop services due to lack of insight into application types and usage patterns, leading to inefficient resource management.
Innovation Solution
Implementing a system that monitors user sessions and predicts future resource requirements by tracking historical data, current usage, and anticipated user sessions to dynamically allocate and manage computing resources across virtual machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hypervisors allocate resources based on current usage only, then resource allocation is simple, but future resource demands cannot be predicted accurately leading to inefficient resource management
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical session data before future resource demands occur. The session analysis component continuously monitors and stores session information, enabling the prediction component to forecast future resource needs based on established patterns, rather than reacting to current usage only.
Solution Approach 2:
The system implements feedback mechanisms where prediction accuracy is continuously improved by comparing predicted resource demands with actual usage. The machine learning models are retrained with new session data, creating a feedback loop that enhances prediction precision over time while adapting to changing usage patterns.
2Reliability
If more computing resources are allocated to virtual machines, then service quality improves, but operational costs increase
Solution Approach 1:
The system enables dynamic resource allocation where virtual machine resources are adjusted in real-time based on predicted session demands. The resource allocation component continuously modifies resource assignments to match forecasted needs, ensuring service quality is maintained during peak periods while reducing allocations during low-utilization periods to minimize operational costs.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on prediction outcomes. When predictions indicate low session activity, resource parameters such as CPU allocation, memory, and storage are reduced. When high activity is predicted, parameters are increased accordingly, optimizing the balance between service quality and operational cost.
3Productivity
If historical session data is collected and analyzed, then future resource demands can be predicted, but data processing complexity increases
Solution Approach 1:
The system implements self-service through automated session analysis and prediction processes. The session analysis component automatically collects, processes, and stores session data without manual intervention, while the prediction component autonomously generates forecasts using machine learning algorithms, reducing the need for complex manual data processing while maintaining high resource management efficiency.
Solution Approach 2:
The system uses machine learning models that create simplified representations or copies of complex usage patterns. Instead of processing all raw session data directly for each prediction, the system trains models on historical data to create compact predictive representations, significantly reducing data processing complexity while maintaining prediction accuracy.
Data Source
AI summary
Disclosed are various approaches to anticipating future resource consumption based on user sessions. A message comprising a prediction of a future number of concurrent user sessions to be hosted by a virtual machine within a predefined future interval of time is received. It is then determined whether the future number of concurrent user sessions will cause the virtual machine to cross a predefined resource threshold during the predefined future interval of time. Then, a message is sent to a first hypervisor hosting the virtual machine to migrate the virtual machine to a second hypervisor.