DEPLOYING CONTAINER APPLICATIONS THROUGH MACHINE LEARNING: REVIEW AND ANALYSIS

Due to the dynamic development and growing complexity of systems, it is necessary to constantly search for new methods for managing services in cloud computing. Consequently, containerization technologies help to easily deploy applications, manage and distribute cloud provider resources, thereby scaling them, ensure data portability and isolate applications and their dependencies. However, with the growing complexity of infrastructure and data, new challenges, such as ensuring continuous operation of services, resource optimization, load balancing, and system performance, arise. The use of machine learning becomes necessary to solve the many problems that arise in the context of deploying and managing containerized applications. The integration of containerization technology and machine learning allows you to adapt to changing conditions, optimize the use of resources and ensure continuous operation of the system. The article provides an overview of popular machine learning methods and their application in the context of container technologies, presented a reference architecture for container orchestration based on machine learning and their evolution, and also explored containers and orchestrators such as Kubernetes, Docker, Prefect, Nomad, Red Hat OpenShift Service on AWS, Amazon Elastic Container Service (ECS), Google Kubernetes Engine, Azure Kubernetes Service. Machine learning techniques can be used to predict resource consumption, adapt to demand, predict timing for managing container clusters, and analyze workload behavior based on historical data.