Resource Demand Prediction in Virtualized Hosts
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Solution Overview
Problem
Current resource management solutions lack agility and forecasting precision to match the increasing fluidity and complexity of virtualized computing environments, where workload mobility and dynamic resource allocation create management challenges due to varying demands and fluctuations.
Innovation Solution
The technology employs a multi-variate analysis to predict resource demands by reconstructing historical resource consumption trends based on current consumer and host residencies, accounting for workload patterns and rapid changes, enabling accurate forecasting and flexible resource management in virtualized environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtualization is implemented to achieve greater workload mobility and resource utilization, then resource pooling and flexibility are improved, but resource management complexity and unpredictability increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical resource consumption data before making placement decisions. It reconstructs historical trends and uses this pre-prepared information to predict future resource demands, enabling proactive rather than reactive resource management in virtualized environments
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual resource consumption and comparing it against predicted values. This feedback loop allows the system to refine its predictions and adjust resource allocation strategies based on actual performance data, reducing management complexity through data-driven decision-making
2Productivity
If dynamic resource allocation is implemented to adapt to varying demands, then resource utilization efficiency is improved, but forecasting precision and management agility are insufficient
Solution Approach 1:
The system performs preliminary analysis of historical resource consumption patterns and reconstructs trends before making forecasting decisions. By pre-processing and storing historical data, the system enables more accurate predictions of future resource demands, improving forecasting precision to match dynamic allocation needs
Solution Approach 2:
The system dynamically adjusts resource allocation strategies based on reconstructed historical trends and predicted future demands. It adapts placement decisions in real-time based on changing workload patterns, ensuring both high resource utilization efficiency and accurate forecasting that responds to dynamic conditions
3Measurement precision
If resource placement is optimized based on historical data analysis, then resource allocation accuracy is improved, but computational requirements and data processing complexity increase
Solution Approach 1:
The system segments the complex data processing task into manageable components: collecting individual resource consumption data points, reconstructing historical trends for each resource type, analyzing patterns separately, and then synthesizing predictions. This segmentation reduces overall computational complexity by breaking down the multi-variate analysis into discrete, processable steps
Data Source
AI summary
In some examples, a method can involve collecting resource consumption data for resource consumer objects associated with hosts in a computing environment. The method can involve identifying, for each respective host, a respective set of resource consumer objects at the host and, based on the resource consumption data, determining a projected resource consumption history for each host, the projected resource consumption history being based on a combined resource consumption, over a period of time, associated with the respective set of resource consumer objects currently hosted a the host. The method can involve calculating a projected resource availability for each host based on a respective resource capability of the host and the projected resource consumption history for the host, and selecting a particular host for a resource consumer object based on the projected resource availability of each host.