Advanced topics

This section will explore advanced techniques such as predictive auto-scaling using ML algorithms and integration with cloud-native tools and services, which provide more efficient and intelligent scaling solutions for optimal performance and cost efficiency.

Predictive auto-scaling using ML algorithms

Predictive auto-scaling, a more proactive approach than traditional reactive methods, harnesses ML algorithms to forecast future demand based on historical data and relevant metrics. This allows for optimized resource allocation and improved application performance.

To implement predictive auto-scaling, follow these steps:

1. Collect and preprocess data: Gather historical metrics such as CPU usage, memory usage, network traffic, and request rates using monitoring tools (e.g., AWS CloudWatch, Google Cloud Monitoring, or Azure Monitor). Cleanse and preprocess this data to handle any missing values, and outliers and ensure consistency.
2. Train ML models:...