Virtual Appliance Metric Prediction via Crowd-Sourced Data Analysis
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Solution Overview
Problem
Accurately predicting resource usage and performance characteristics of virtual appliances is challenging due to complex interactions and vast amounts of data, leading to inefficient resource allocation and potential performance issues in cloud environments.
Innovation Solution
A system and method for performing operational metric analysis using application operational data from multiple instances of virtual appliances, which generates predictions for resource requirements and performance metrics by identifying relevant features, analyzing data, and providing confidence factors for better provisioning and deployment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource estimation methods are used for virtual appliances, then resource allocation can be simplified, but prediction accuracy deteriorates due to complex component interactions and non-linear relationships
Solution Approach 1:
The patent introduces an intermediary analysis system that collects and processes operational data from multiple virtual appliance instances. This intermediary layer aggregates data from various components and environments, transforming complex interactions into manageable datasets that can be analyzed to generate accurate predictions without requiring direct analysis of every component interaction.
Solution Approach 2:
The patent utilizes multiple instances (copies) of the virtual appliance deployed in different environments. By collecting operational data from these copies, the system can analyze performance characteristics across various conditions and generate predictions that accurately reflect real-world behavior, avoiding the need to model every complex interaction from scratch.
2Measurement precision
If comprehensive operational data collection is implemented, then prediction accuracy improves, but data processing complexity and computational resources increase
Solution Approach 1:
The patent extracts and focuses on specific operational data points that are most relevant to predicting resource usage and performance characteristics. Rather than processing all possible data, the system identifies and processes only the critical metrics from multiple instances, reducing computational energy consumption while maintaining prediction accuracy.
Solution Approach 2:
The patent processes operational data from multiple instances (more than a single instance) to achieve accurate predictions. This partial action approach processes data selectively across multiple deployments rather than attempting to analyze every possible data point from every instance, balancing computational resources with prediction accuracy.
3Reliability
If resource allocation is optimized based on accurate predictions, then virtual appliance performance improves, but deployment complexity increases
Solution Approach 1:
The patent performs preliminary analysis of operational data from multiple instances before deployment to determine optimal resource allocation. By analyzing data in advance from various environments, the system can pre-determine resource requirements and performance characteristics, simplifying the actual deployment process while ensuring reliable resource allocation.
Solution Approach 2:
The patent uses operational data collected from running instances to generate predictions that feed back into deployment decisions. This feedback loop allows the system to learn from actual performance and adjust resource allocation recommendations, improving reliability while making deployment decisions based on empirical evidence rather than complex manual configuration.
Data Source
AI summary
A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.