Applied Intelligence and Computing
Mathematical Approaches to Securing Kubernetes: Analyzing Log Data Volume in a Complex Landscape
Authors: Gobinda Karmakar and Harwant Singh Arri
Publishing Date: 10-11-2024
ISBN: 978-81-955020-9-7
Abstract
Kubernetes, the de facto standard for container orchestration, has revolutionized the deployment and management of applications at scale. However, the increasing complexity of Kubernetes environments has led to an exponential growth in the volume of logs generated by various components, including Kubernetes itself, containerized applications, and the underlying infrastructure. This paper presents a comprehensive exploration of the challenges associated with log management in complex Kubernetes environments. We introduce a mathematical model to quantify the volume of logs, represented as nV, where n is the number of components and V is the volume of logs per unit time. Effective log management, crucial for maintaining the security and operational integrity of Kubernetes clusters, becomes increasingly challenging as n increases. This paper proposes innovative strategies for navigating this deluge of logs, ensuring a secure and resilient Kubernetes deployment. Our findings provide valuable insights for organizations striving to maintain robust Kubernetes environments amidst escalating complexity.
Keywords
Elasticsearch, Logstash, and Kibana (ELK stack), Role-based access control (RBAC), SPSS (Statistical Package for the Social Sciences), Artificial Intelligence (AI), ANOVA, Kubernetes.
Cite as
Gobinda Karmakar and Harwant Singh Arri, "Mathematical Approaches to Securing Kubernetes: Analyzing Log Data Volume in a Complex Landscape", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2024, pp. 177-193. https://doi.org/10.56155/978-81-955020-9-7-19