Cloud computing has revolutionized the IT landscape, offering scalable and cost-efficient solutions for data storage and access. In recent years, multi-cloud environments have emerged as a strategic approach to leverage the strengths of various cloud service providers, mitigating vendor lock-in risks and optimizing performance. In the paper, we propose an improved ontological model to formalize the domain's concepts and relationships. This comprehensive strategic approach aims to enhance data management practices automation and facilitate informed decision-making in multi-cloud environments. The main idea focuses on combining a two scaling mechanism together in the same time. The paper focuses on scaling of containerized applications in Elastic Kubernetes Service (EKS) on the AWS platform using Kubernetes Cluster Autoscaler (KCA) and Auto Scaling Group (ASG). Theoretical provisions are supported by experimental studies. Simulations were conducted that included automatically adding and removing EC2 instances running ASG and comparing the results to other scaling methods. The results provide insight into the effectiveness of ASG in combination with Cluster Autoscaler to provide performance indicators, stability and cost of scaling in various load servicing scenarios.