Predictive scaling is one of the most attractive characteristics of AI monitoring. In the classic scaling, resources are increased in response to demand fluctuations after demand has already occurred, which leads to certain ineffectiveness. On the other hand, AI models study historical data to predict the future requirements of resources well in advance. Such forecasts reduce cases of overexploitation, increase performance and facilitate the effective billing of clouds.
For example, dynamically allocation of resources requires the use of automatic learning algorithms to assess the trends of use. A predictive scaling system based on AI could anticipate traffic peaks during peak hours and provide additional calculation power before performance is affected. Thus, it improves the user experience, backup of downtime and optimizes costs, ensuring that organizations only pay the resources they need.
