Hierarchical and Balanced Data Structures In Kubernetes

Authors: SatyaRam Tsaliki, Dr.B.PurnachandraRao

Country: United States

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Abstract: ETCD is a distributed key-value database that offers a dependable solution for storing and managing data in distributed environments. This section provides insight into ETCD's functionality and its importance in Kubernetes. ETCD guarantees data durability and consistency across multiple nodes, supports distributed locks to avoid simultaneous modifications, and enables leader election for distributed systems. It utilizes the Raft consensus algorithm to handle data replication and ensure uniformity across nodes. ETCD nodes form clusters that enhance data reliability and availability. It stores information as key-value pairs, provides real-time watchers for monitoring key changes, and supports leases to manage distributed locks and resource allocation. ETCD acts as the primary backend storage for Kubernetes, storing essential cluster information such as node metadata, pod statuses, and replication controller details, along with configuration data like secrets, persistent volume claims, and config maps, as well as network policies and rules. Its high availability ensures both consistency and accessibility across nodes, while distributed locks safeguard data integrity by preventing conflicts. Additionally, ETCD scales efficiently to support large Kubernetes clusters. When a configuration change is applied via kubectl or other clients, the Kubernetes API Server verifies, authorizes, and sends the updates to ETCD. ETCD processes the changes, stores the updated configuration in its key-value store, and synchronizes the data across its cluster to maintain consistency. As the core storage system of the Kubernetes cluster, ETCD preserves the cluster's state by maintaining its latest data in a key-value format. This paper focuses on implementing ETCD using balanced tree data structures, comparing the Adelson-Velsky Landis Tree with the B-Tree. This paper highlights scenarios where B-Trees outperform logarithmic height tree Trees and aims to demonstrate the superior performance of B-Trees for ETCD implementation.

Keywords: Service, IP-Tables, StatefulSets, ReplicaSets, Deployments, Load Balancer, Kubernetes (K8S), Nodes, Pods, Cluster, Service Abstraction, ETCD.

Paper Id: 231912

Published On: 2021-06-01

Published In: Volume 9, Issue 3, May-June 2021

Cite This: Hierarchical and Balanced Data Structures In Kubernetes - SatyaRam Tsaliki, Dr.B.PurnachandraRao - IJIRMPS Volume 9, Issue 3, May-June 2021.

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