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Log Structured Hash Table Tree For Efficient CPU Utilization In ETCD

Author(s) Renukadevi Chuppala, Dr.B.PurnachandraRao
Country United States
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, and 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 Log Structured Merge (LSM) and Log Structured Hash Table (LHST) Tree. Log Structured Hash Table Tree outperforms Log Structured Merge , LSM in some scenarios. We will work on to prove that Log Structured Hash Table Tree implementation provides better CPU Utilization than Log Structured Merge LSM Tree CPU utilization.
Keywords Kubernetes (K8S), Cluster, Nodes, Deployments, Pods, ReplicaSets, Statefulsets, Service, IP-Tables, Load Balancer, Service Abstraction, , Adelson-Velsky and Landis (LSM), Log Structured Merge Tree (LSM) Tree, Log Structured Hash Table Tree (LHST), ETCD.
Published In Volume 5, Issue 6, June 2024
Published On 2024-06-06
Cite This Log Structured Hash Table Tree For Efficient CPU Utilization In ETCD - Renukadevi Chuppala, Dr.B.PurnachandraRao - IJLRP Volume 5, Issue 6, June 2024.
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