Efficient Resource Allocation in Kubernetes Using Machine Learning

Abstract

Kubernetes is a distinguished open-source container orchestration system in cloud computing and containerized applications. Google developed it, and the Cloud Native Computing Foundation now maintains it. Kubernetes offers a robust framework for automating application deployment scaling and management, revolutionizing how organizations use their containerized workloads and providing huge flexibility and feasibility. The current paper explores the application of machine learning algorithms to optimize resource allocation in Kubernetes environments. As the complexity of cloud-native applications increases because of various engagements, it is vital to maintain performance and cost- effectiveness. This study also evaluates various machine learning models and techniques and their relevancy in areas such as anomaly detection and enhancing overall cluster utilization. Our findings include machine learning-driven methodologies that will significantly improve performance utilizing historical data. Kubernetes's decentralized nature requires a scalable structure for task scheduling to accommodate dynamic workloads conveniently. The AIMD algorithm, a celebrated method for congestion avoidance in network management, inspires our approach. Computing clusters can be challenging to deploy and manage due to their complexity. Monitoring systems collect large amounts of data, which is daunting to understand manually. Machine learning provides a viable solution to detect anomalies in a Kubernetes cluster. KAD (Kubernetes et al.) is one such algorithm that can solve the Cluster anomalies problem. Enormous Cloud native applications market is projected to reach 17.0 billion USD by 2028, which was USD 5.9 billion in 2023. On par with those numbers is the global Machine Learning (ML) market size, which was valued at $19.20 billion in 2022 and is expected to grow from $26.03 billion in 2023 to $225.91 billion by 2030 (As per Fortune Business Insights). At the conjecture, both markets will take innovation to a new level, offering more adaptive solutions for contemporary cloud infrastructures.