Automated Cloud Instance Selection via Performance Metrics
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Solution Overview
Problem
In cloud computing environments, selecting the appropriate instance family for an application is challenging due to varying resource requirements and compliance needs, often leading to inefficient resource usage and increased costs.
Innovation Solution
A method and system that automate the selection of instance families by testing different instance families using a target application, collecting performance metrics, and ranking instance families based on these metrics, considering both performance and cost factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If instance selection is based on service provider suggestions or manual estimation, then the selection process is simple and quick, but the resource efficiency and cost optimization are compromised
Solution Approach 1:
The system enables self-service through automated instance selection by having the application itself generate performance data during normal operation. The application acts as its own test subject, automatically collecting metrics and providing feedback for instance family recommendations without requiring external testing infrastructure or manual intervention.
Solution Approach 2:
The system implements continuous feedback loops where performance metrics collected from application execution are fed back into the instance selection model. This feedback mechanism allows the system to learn from actual performance data and continuously improve instance family recommendations, transforming static provider suggestions into dynamic, data-driven decisions.
2Measurement precision
If comprehensive performance testing is conducted across multiple instance families, then accurate instance selection is achieved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by having the application continuously collect performance metrics during normal operation before formal instance selection is needed. This pre-collection of data during the application's natural lifecycle eliminates the need for separate, time-consuming testing phases while ensuring accurate performance measurements are available for instance family evaluation.
3Reliability
If more performance metrics are collected and analyzed, then the instance selection quality improves, but the system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a unified data collection and analysis framework that handles multiple performance metrics simultaneously. The same infrastructure that collects basic performance data also generates instance family recommendations, eliminating the need for separate specialized systems for each function and reducing overall system complexity despite handling comprehensive metrics.
Data Source
AI summary
A system or method for selecting an instance family for deploying a target application. The system accesses a configuration that specifies a target application, relevant performance metrics, and various instance families that are candidates for deployment. For each instance family, the target application is temporarily deployed during a specified test period, and the performance metrics are recorded and stored. These metrics are then analyzed to generate rankings corresponding to the instance families. Based on these rankings, a recommendation is generated for an instance family for the application deployment based on ranking metrics.