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From Reactive to Proactive: AI-Enhanced Performance Insights in Kruize
Summary of Idea:
In dynamic containerized environments, predicting and preventing performance bottlenecks is crucial for maintaining efficiency and cost-effectiveness. Kruize provides container right-sizing recommendations based on performance metrics such as CPU and memory usage, collected from Prometheus. Currently, recommendations are generated using a percentile-based approach.
To enhance this, we propose integrating a time series and regression-based models into Kruize. These models will analyze historical performance data to generate more proactive, data-driven recommendations. The integration of these models will improve predictive accuracy, enable real-time and batch data processing, and ultimately help users optimize their infrastructure more effectively.
Project Features:
- Collect historical usage data from Prometheus, perform feature engineering (e.g., smoothing, normalization, trend extraction) to enhance model accuracy.
- Store processed data efficiently for real-time and batch analysis.
- Implement and train a time series model (e.g., ARIMA, Prophet, or LSTM).
- Develop and evaluate a regression-based model (e.g., Random Forest, XGBoost).
- Compare their performance in terms of prediction accuracy, latency, and robustness.
- Integrate both models into Kruize to generate performance recommendations.
Knowledge Prerequisite:
- Languages: Python, Java, Shell Scripting
- Technologies: Scikit-Learn, Pandas, NumPy, Matplotlib
- Machine Learning Concepts: Time Series Analysis, Regression Models, Forecasting
- Experience with containerized environments, shell scripting and performance tuning is a plus.
GitHub Repository: https://github.com/kruize/autotune.git
Project Size: Medium (~200 Hours)
Skill Level: Intermediate
Contact(s) / Potential Mentor(s):
- Shekhar Saxena (shesaxen@redhat.com)
- Rebecca Whitworth (rsimmond@redhat.com)
Associated Community Project(s):
- Kruize (https://github.com/kruize/autotune)