AUTOMATIC SCALATION METHODS IN CLOUD ENVIRONMENTS

Abstract

This article examines the main approaches to resource scaling in the cloud, as well as the possibilities for automation and integration with machine learning. It describes three types of scaling: vertical, horizontal, and automatic (which combines the first two). Special attention is paid to automatic scaling, which allows the system to dynamically respond in real time. By using pre-configured triggers, you can add or remove resources “on the fly,” ensuring stable and cost-effective application performance. However, incorrectly set triggers or poor monitoring can lead to either an excess or a shortage of resources. The main section focuses on the use of machine learning for load forecasting, such as LSTM models, which can “learn” from historical data, identify long-term patterns, and recognize them. This approach allows you to respond in advance by increasing or decreasing resources before they become excessive or insufficient. In the practical part of the article, using Azure as an example, it shows how to integrate a machine learning model with cloud autoscaling tools to improve resource management and reduce downtime and costs. The conclusion explains that each type of scaling has its pros and cons. Vertical scaling may be best for stable loads and monolithic applications. Horizontal scaling works better for distributed systems with a large number of users. Implementing automatic scaling with load forecasting based on machine learning opens up the possibility of more accurate load predictions and cost-effective use of cloud resources. It requires in-depth knowledge, careful configuration, and continuous data collection, but it enables companies to build flexible, resilient, and economically viable cloud systems.