Adaptive Cloud Configuration Selection via Bayesian Optimization
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Solution Overview
Problem
Existing approaches for selecting cloud configurations for big data analytics in cloud computing are often inaccurate, require high overhead, and lack adaptivity to diverse applications and cloud configurations, making it challenging to minimize costs and running times effectively.
Innovation Solution
An adaptive performance model is used to iteratively generate and evaluate candidate cloud configurations, updating the model based on performance data until a confidence threshold is met, employing Bayesian Optimization to reduce search costs and times while ensuring accuracy and adaptivity across various applications and configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing approaches are used for identifying cloud configurations, then the process is simple, but accuracy is low and overhead is high
Solution Approach 1:
The system dynamically adapts the performance model to different applications and cloud configurations through iterative updates. The model evolves based on observed performance data, allowing it to adjust to diverse analytical tasks and configuration types without requiring manual reconfiguration or complex predefined rules for each scenario.
Solution Approach 2:
The system changes parameters of the performance model iteratively based on observed performance data. By updating model parameters with actual performance measurements from candidate configurations, the system improves accuracy over time while maintaining a unified model structure that avoids the complexity of multiple specialized models.
2Measurement precision
If exhaustive search methods are used to identify optimal cloud configurations, then accuracy improves, but search time and costs increase significantly
Solution Approach 1:
The system performs preliminary actions by generating candidate cloud configurations based on application requirements before actual deployment. The performance model predicts performance metrics for these candidates, allowing the system to evaluate multiple options in advance and select the optimal configuration without exhaustive testing of all possible configurations.
Solution Approach 2:
The performance model acts as an intermediary between application requirements and cloud configuration selection. Instead of directly testing all configurations, the model mediates by predicting performance outcomes, enabling the system to identify optimal configurations with high accuracy while avoiding the time and cost of exhaustive searches.
3Adaptability or versatility
If static performance models are used, then implementation is straightforward, but adaptability to different applications and configurations is poor
Solution Approach 1:
The performance model is designed to be dynamic rather than static. It automatically adapts to different applications and cloud configurations by iteratively updating its parameters based on observed performance data. This dynamic nature enables the model to handle diverse analytical tasks and configuration types without requiring manual intervention or complex reconfiguration.
Solution Approach 2:
The performance model performs self-service by automatically updating itself with observed performance data. Through iterative learning from actual configuration performance, the model improves its accuracy and adaptability autonomously, eliminating the need for complex external tuning or manual adjustments for different applications and configurations.
Data Source
AI summary
Provided are methods and systems for facilitating selection of a cloud configuration for deploying an application program with high accuracy, low overhead, and automatic adaptivity to a broad spectrum of applications and cloud configurations. The methods and systems are designed for building a performance model of cloud configurations, where the performance model is capable of distinguishing an optimal cloud configuration or a near-optimal cloud configuration from other possible configurations, but without requiring the model to be accurate for every cloud configuration. By tolerating the inaccuracy of the model for some configurations (but keeping the accuracy of the final result) it is possible to achieve both low overhead and automatic adaptivity: only a small number of samples may be needed and there is no need to embed application-specific insights into the modeling.