ETCD IMPLEMENTATION USING LOG STRUCTURED MERGE TREE TO IMPROVE CPU UTILIZATION

Authors: Kishore Kumar Jinka, Dr.B.PurnachandraRao

Country: United States

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Abstract: ETCD is a distributed key-value store that provides a reliable way to store and manage data in a distributed system. Here's an overview of ETCD and its role in Kubernetes. ETCD ensures data consistency and durability across multiple nodes, provides distributed locking mechanisms to prevent concurrent modifications, facilitates leader election for distributed systems. Etcd uses a distributed consensus algorithm (Raft) to manage data replication and ensure consistency across nodes. Etcd nodes form a cluster, ensuring data availability and reliability. stores data as key-value pairs., provides watchers for real-time updates on key changes, supports leases for distributed locking and resource management, Etcd serves as the primary data store for Kubernetes, responsible for storing and managing Cluster state i.e, Node information, pod status, and replication controller data, Configuration data like Persistent volume claims, secrets, and config maps, Network policies i.e, Network policies and rules, High availability that ensures data consistency and availability across nodes, Distributed locking i.e, Prevents concurrent modifications and ensures data integrity. Scalability Supports large-scale Kubernetes clusters. When ever we are sending apply command using kubectl or any other client API Server authenticates the request, authorizes the same, and updates to etcd on the new configuration. Etcd receives the updates (API Server sends the updated configuration to etcd), then etcd writes the updated configuration to its key-value store. Etcd replicates the updated data across its nodes and it ensures data consistency across all the nodes. We can say that ETCD is the main storage of the cluster. It carries the cluster state by storing the latest state at key value store. In this paper we will discuss about implementation of ETCD using Adelson-Velsky and Landis (AVL) and Log Structured Merge (LSM) Tree. Log Structured Merge tree outperforms Adelson-Velsky and Landis , AVL in some scenarios. We will work on to prove that Log Structured Merge Tree implementation provides better CPU utilization than Adelson-Velsky and Landis AVL Tree implementation.

Keywords: Keywords: Kubernetes (K8S), Cluster, Nodes, Deployments, Pods, ReplicaSets, Statefulsets, Service, IP-Tables, Load Balancer, Service Abstraction, , Adelson-Velsky and Landis (AVL), Log Structured Merge Tree (LSM) Tree, ETCD

Paper Id: 231498

Published On: 2024-06-04

Published In: Volume 12, Issue 3, May-June 2024

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