Predictive scaling is the best-case approach that any organization wants to take. Often, you can collect historical data of application workload. For example, an e-commerce website such as Amazon may have a sudden traffic spike, and you need predictive scaling to avoid any latency issues. Traffic patterns may include the following:
- Weekends have three times more traffic than a weekday.
- Daytime has five times more traffic than at night.
- Shopping seasons, such as Thanksgiving or Boxing Day, have 20 times more traffic than regular days.
- Overall, the holiday season in November and December has 8 to 10 times more traffic than during other months.
You may have collected the previous data based on monitoring tools that are in place to intercept the user's traffic, and based on this, you can make a prediction for scaling. Scaling may include planning to add more servers when workload increases, or to add additional caching. This example of an e-commerce workload is one...
