ML-based Heterogeneous Container Orchestration Architecture

In recent years, the popularity of containerization technologies has been growing. When they are used, computational tasks are placed in lightweight containers that can be easily moved between different computing nodes. Containerization using Docker is especially popular at the moment. The use of these solutions opens up enormous opportunities for building distributed and cluster computing systems. To maintain the operability of such systems, special tools are used, and one of them is an orchestrator. However, existing orchestrators are focused on not-so-large computing systems in which performance can be maintained by simply moving computational tasks from non-working nodes to working ones. In large systems with many nodes and a huge number of computational tasks, it is also necessary to take into account the uneven consumption of resources by various tasks. This article proposes a system architecture that can solve the problem of container orchestration using machine learning methods and given the uneven consumption of resources by