Adaptive Resource Optimization in Containerized Environments Using Particle Swarm Optimization and Decision Tree Classification

Abstract

Containerization has emerged as a powerful technology for deploying and managing applications. However, an efficient resource allocation in container-based cloud environments remains a significant challenge. This paper proposes a novel approach to adaptively optimize resource allocation using a combination of Particle Swarm Optimization (PSO) and Decision Tree Classification. PSO is employed to explore the solution space and identify optimal resource configurations. To enable predictive modelling for future resource needs, Decision Tree Classification is used to identify patterns in historical resource utilization. Through the integration of these two methods, our approach seeks to optimize performance and cost-efficiency in containerized environments by modifying resource allocation in response to dynamic workload fluctuations we have compared results with existing PSO algorithms in which our results are improved for container resource allocation.