Kubernetes IP-tables Performance using Trie Tree and Radix Tree Implementation

Renukadevi Chuppala; B.PurnachandraRao

Kubernetes IP-tables Performance using Trie Tree and Radix Tree Implementation

Author(s)	Renukadevi Chuppala, B.PurnachandraRao
Country	United States
Abstract	Kubernetes (K8s) is an open-source container orchestration platform designed to automate the deployment, scaling, and management of containerized applications. Developed originally by Google and now managed by the Cloud Native Computing Foundation (CNCF), Kubernetes has become the de facto standard for container management due to its scalability, flexibility, and reliability in running production-grade workloads. Containers package applications and their dependencies in isolated environments, ensuring that they run the same regardless of the host environment. Docker is one of the most well-known container platforms, but others like rkt and CRI-O are also compatible with Kubernetes. Service abstraction refers to how Kubernetes abstracts the way applications running inside the cluster are exposed to the outside world or internally within the cluster. A Service in Kubernetes is an abstraction layer that defines a logical set of Pods and a policy by which to access them. The main goal of the service abstraction is to decouple the application logic from the actual deployment of Pods, allowing the application to scale or self-heal without requiring manual updates to other parts of the infrastructure. In Kubernetes, IP Tables plays a key role in how networking is managed, particularly in terms of routing traffic to Pods and Services. Kubernetes uses IPTables (via the Linux kernel) in several key components to ensure smooth communication within the cluster and to external systems. Kubernetes uses IPTables to implement the Service abstraction. When you create a Service, Kubernetes sets up IPTables rules to route traffic to the correct set of Pods. For a ClusterIP service, Kubernetes creates IP Tables rules that intercept traffic to the service's IP and port, then routes the traffic to one of the Pods that match the service's selector. This enables round-robin load balancing between Pods. Existing kuberenets is using Trie tree implementation for IP tables for matching the search criteria. In this paper we will prove the performance improvement of ip tables by using the radix tree implementation for search criteria.
Keywords	Kubernetes (K8S), Cluster, Nodes, Deployments, Pods, ReplicaSets, Statefulsets, Service, IP-Tables, Trie Tree, Radix Tree, Load Balancer, Service Abstraction.
Field	Computer > Design
Published In	Volume 5, Issue 2, March-April 2023
Published On	2023-04-25
Cite This	Kubernetes IP-tables Performance using Trie Tree and Radix Tree Implementation - Renukadevi Chuppala, B.PurnachandraRao - IJFMR Volume 5, Issue 2, March-April 2023.

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5 Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Kubernetes IP-tables Performance using Trie Tree and Radix Tree Implementation

Share this