Papers by Renukadevi Chuppala
International Journal of Leading Research Publication (IJLRP)
ETCD is a distributed key-value store that provides a reliable way to store and manage data in a ... more 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.
International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2024
Hadoop is an open-source version of the MapReduce Framework for distributed processing. A Hadoop ... more Hadoop is an open-source version of the MapReduce Framework for distributed processing. A Hadoop cluster possesses the capacity to manage substantial volumes of data. Hadoop utilizes the Hadoop Distributed File System, also known as HDFS, to manage large amounts of data. The client will transfer data to the DataNodes by retrieving block information from the NameNode. The pipeline configuration will connect the DataNodes that store the blocks. If a DataNode or network fails during the data writing process, the pipeline will remove the failed DataNode. The pipeline will add the new DataNode based on the existing DataNodes in the cluster. If there is a scarcity of spare nodes in the cluster, customers may encounter an abnormally high frequency of pipeline failures due to the inability to locate additional DataNodes or replacements. In the event of a network failure, the data packet is unable to reach the target DataNode due to their interconnected pipeline structure. Interconnecting each DataNode with every other DataNode ensures that multiple pathways are available through other DataNodes, thereby preventing network failure. The copy operation will take longer due to pipeline connectivity. On the other hand, a direct connection between a DataNode and all other DataNodes significantly reduces the time required, as the datapacket doesn't have to traverse through all other DataNodes to reach the final DataNode. This paper presents the utilization of the A* algorithm to enhance the performance of write operations in the Hadoop Distributed File System.
International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences
Etcd is a distributed key-value store that provides a reliable way to store and manage data in a ... more 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 BTree and Factal Tree. Factal tree outperforms BTree in some scenarios. We will work on to prove that Factal Tree implementation provides better performance than BTree.
International Journal of Innovative Research and Creative Technology , 2023
Kubernetes is a platform for automating the deployment, scaling, and management of containerized ... more Kubernetes is a platform for automating the deployment, scaling, and management of containerized applications. Kubernetes automates the orchestration of containers, enabling seamless scaling, load balancing, and fault tolerance in a highly dynamic environment. In Kubernetes, iptables is commonly used to support networking functionalities like kube-proxy, Manages networking rules to route traffic to the appropriate backend pods. Service discovery and load balancing. Kubernetes uses iptables rules to direct requests for a service to the correct pod(s) based on IPs. Network policies, IP Tables plays a key role in how networking is managed, particularly in terms of routing traffic to Pods and Services. Kubernetes uses IPTables in several key components to ensure smooth communication within the cluster and to external systems. When you create a Service, Kubernetes sets up IPTables rules to route traffic to the correct set of Pods. IP Tables use Hash Table to store rules and connections for fast lookups. Linked lists to manage chains of rules and connections, trees to optimize rule matching and bitmaps for compactly store flags and options. IP Hashset is commonly used to optimize IPTables performance, especially in large-scale environments with extensive IP filtering needs, like Kubernetes clusters. When dealing with numerous IP addresses or IP ranges, the hashset data structure allows more efficient storage and quicker lookups than a list, especially with large datasets. Hashsets are typically implemented through ipset in Linux, which works in conjunction with iptables. Single IP hash sets in iptables can lead to significant challenges, especially as cluster sizes and network complexity grow. A single IP hash set can struggle to handle the scale in large Kubernetes clusters, where thousands of IP addresses are stored. It becomes more memory-intensive and slower in lookups due to higher collision rates in the hash set. As the hash set grows, search and update operations slow down, creating latency in packet processing. This affects cluster performance, as any latency in the network layer impacts the efficiency of service communication. In this paper we will address all these issues by using multi IP hash set.
International Journal for Multidisciplinary Research (IJFMR) , 2023
Kubernetes (K8s) is an open-source container orchestration platform designed to automate the depl... more 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.
International Journal of Applied Engineering & Technology , 2023
Sonatype Nexus is a powerful repository manager widely used in DevOps and continuous integration/... more Sonatype Nexus is a powerful repository manager widely used in DevOps and continuous integration/continuous delivery (CI/CD) pipelines. It is designed to manage, store, and retrieve binary artifacts efficiently. Nexus plays a crucial role in software development by providing centralized storage and management for build artifacts, dependencies, and containers, enabling teams to collaborate more effectively and maintain control over their software supply chain. As part of the business activity on daily basis , lots of artifacts will be uploaded to number of repositories to each nexus instance. The space will be consumed proportional to the volume of artifacts. It is administrator's responsibility to clear the space as and when it reaches to beyond the thresh hold limit. If the admin misses to clear the space it leads to the situation to shuts down the server, which will effect the entire business. Admin can use the available automated tasks from the nexus admin tasks, but they are having some limitations on the deletion of artifacts, we need to opt only unwanted artifacts. But in the case of high usage of wanted artifacts the space will get decreased and we can't know when the server will go down. This paper resolves this issue by providing the solution using machine learning algorithm (Linear Regression Analysis) on the usage of space by users. It will findout the regression equation for the given data , so that we can find out prediction value for each actual value. Using the regression equation we can predict the value.
Advanced International Journal of Multidisciplinary Research E-ISSN: 2584-0487
ETCD is a distributed key-value store that provides a reliable way to store and manage data in a ... more 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 performance than Adelson-Velsky and Landis AVL Tree.
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Papers by Renukadevi Chuppala