Optimizing Resource Allocation in Containerized Environments with AI-driven Performance Engineering

Abstract

In the rapidly evolving landscape of cloud computing, containerized environments have become a cornerstone for deploying scalable and efficient applications. However, optimizing resource allocation within these environments poses significant challenges due to their dynamic and complex nature. Traditional methods often fall short in addressing the intricacies of resource management, leading to suboptimal performance and increased operational costs. This paper explores the integration of Artificial Intelligence (AI) with performance engineering to enhance resource allocation in containerized environments.We propose an AI-driven framework that leverages machine learning algorithms to predict workload demands and dynamically allocate resources accordingly. The framework employs a combination of supervised learning for predicting future resource needs based on historical data, and reinforcement learning for real-time resource management and optimization. By continuously analyzing performance metrics and workload patterns, the AI models can make informed decisions on scaling resources up or down, thus ensuring optimal performance while minimizing waste.