In a globalized world, international expansion has become much more accessible. But despite this, entering new markets is still a difficult marathon that requires clear planning and careful preparation. In most cases, system expansion can take years or even longer, depending on the specifics of the product and the chosen method of market penetration.
The article delves into the intricate challenge of scaling microservices within a Kubernetes cluster, thoroughly examining existing methodologies for scaling microservice architectures, and presenting a novel approach that involves migrating specific components. Unlike the conventional horizontal and vertical scaling strategies, which require additional resources, this proposed method focuses on migrating non-critical components to another Kubernetes cluster.
With the rapid development of information technology, which is widely used in all spheres of human life and activity, extremely large amounts of data have been accumulated today. By applying machine learning methods to this data, new practically useful knowledge can be obtained. The main goal of this paper is to study different machine learning methods for solving the classification problem and compare their efficiency and accuracy.