Advancing Kubernetes Network Stack for High-Performance AI/ML Workloads

Anila Gogineni

doi:10.71097/IJSAT.v15.i4.2260

Advancing Kubernetes Network Stack for High-Performance AI/ML Workloads

Author(s)	Anila Gogineni
Country	United States
Abstract	Network function virtualization diversity involves using a collection of thin replicas to provide network services with the necessary processing power. Redundancy increases network dependability by offering a certain number of copies in case of function failures. Virtual network operations deployment and management through automation is referred to as Kubernetes. Current Kubernetes tools need a resource type that may offer necessary tasks in tandem while taking VNF redundancy and diversity into account. This paper explores Kubernetes networking research along with all its components and explains its benefits for running AI and ML workloads. The networking approach defined by Kubernetes puts obstacles in the way of handling high-performance workloads within cloud-native app administration. The communication between Kubernetes clusters functions through important elements that include CNI plugins and service meshes and ingress controllers which ensure scalability and automated operation. Kubernetes provides essential benefits to AI/ML applications which improve resource allocation and workload distribution and operational efficiency, the study confirms. The research examines both methods to improve Kubernetes network capabilities for AI/ML tasks and innovative scheduling approaches and protective frameworks for enhancing security. The literary analysis examines modern research about container orchestration and security policies and performance optimization techniques. Research highlights key obstacles during AI/ML operation in Kubernetes environments because performance enhancement needs smarter monitoring and optimized resource management to defeat operational complexity and security problems and monitoring limitations.
Keywords	Kubernetes Networking, High-Performance Computing, AI-Driven Optimization, Cloud Environments, Machine Learning
Field	Engineering
Published In	Volume 15, Issue 4, October-December 2024
Published On	2024-11-05
Cite This	Advancing Kubernetes Network Stack for High-Performance AI/ML Workloads - Anila Gogineni - IJSAT Volume 15, Issue 4, October-December 2024. DOI 10.71097/IJSAT.v15.i4.2260
DOI	https://doi.org/10.71097/IJSAT.v15.i4.2260
Short DOI	https://doi.org/g869w2

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Advancing Kubernetes Network Stack for High-Performance AI/ML Workloads

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