International Journal of Scientific and Research Publications, 2022
Hadoop Remote Procedure Call (RPC) is increasingly used in many data centers of known technology ... more Hadoop Remote Procedure Call (RPC) is increasingly used in many data centers of known technology companies such as Facebook and Yahoo together with other data middleware centers like MapReduce, HDFS (Hadoop Distributed File System) and Hbase. Due to its simplicity, efficiency, high performance use of RPC systems, it is essential to achieve dense data storage and low latency and high throughput. This thesis describes how to improve data storage, throughput, and latency performance for big data. E.Sivaraman Dr.R.Manickachezian(2014) said systems are becoming increasingly data-intensive due to the explosion of data and the need to process it, therefore ,the application performance is vital to storage management in data-intensive computing systems. The author mentioned above highligted that researchers believe that applications sensitive areas are advancing in storing large volume of data. As data volumes continue to grow, the movement of data from memory to storage systems has become a critical performance bottleneck for many data centers. However, data management on such multi-server storage systems is yet under studies to ascertain on how to utilize various storages for efficient data movement is an important research topic in RPC data perception. In this research, we propose an adoptive Heartbeat system, data-aware distribution that efficiently manages storage systems to coordinate multiple concurrent data-intensive applications. Extensive experiments are conducted and the results show that this method can significantly improve the network throughput performance and data-intensive storage of Hadoop in HDFS clusters. The thesis discusses the background of RPC, huge data application storage systems, related work and technologies, Hadoop installation, adaptive model mechanisms, evaluation and performance analysis of big data architecture and its server interfaces. The reason to focus on storage improvements is because of the great need to store information globally. A large class of communication-intensive distributed applications and software components have been ported to virtual machines, such as highperformance storage systems. This method dynamically adjusts the RPC configuration between the NameNode and the DataNode by sensing the data characters stored in the DataNode. This method can effectively reduce the processing pressure of the NameNode and improve the network throughput generated by the information transmission between the NameNode and the DataNode.
A large volume of data created on daily basics from modern information systems and latest digital... more A large volume of data created on daily basics from modern information systems and latest digital technologies such as cloud of computing and the user while handling huge dataset meets Internet of Things and difference challenges. Big Data referred to as a large data of single data set of related data as compared to separate tiny datasets with similar amount of data stored in it. Big Data can be a constantly rising from a few dozen terabytes to many petabytes ranging from data. Big data can also be termed as a combination of big and complex data sets that have the vast volume of data, social media analytics, data management efficiency, real-time data. Big Data has several dimensions such as volume, variety, velocity and veracity .Big data processing involves a method called Hadoop, which uses the Mapreduce paradigm to process the data. In this regards, big data analysis is a current trending area of research and development around the world with an aim of improving data storage demands. The basic objective of this paper is to explore the potential impact of Big Data; Data processing, and Big Data Applications and Technologies, challenges and Hadoop associated with it. As a result, this article provides a platform to explore big data at various phases. Furthermore, it extends new chances for scholars to develop the solution, based on the challenges and open research issues.
This Scientific paper focuses on employment of artificial neural network techniques to develop i... more This Scientific paper focuses on employment of artificial neural network techniques to develop in-network "intelligent computation" capability for wireless sensor networks to improve their functionality, utility and survival aspects. The main purpose is to introduce computational intelligence capability for the wireless sensor networks to become adaptive to changes within a variety of operational contexts and to exhibit intelligent behavior. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper.
International Journal of Scientific and Research Publications, 2022
Hadoop Remote Procedure Call (RPC) is increasingly used in many data centers of known technology ... more Hadoop Remote Procedure Call (RPC) is increasingly used in many data centers of known technology companies such as Facebook and Yahoo together with other data middleware centers like MapReduce, HDFS (Hadoop Distributed File System) and Hbase. Due to its simplicity, efficiency, high performance use of RPC systems, it is essential to achieve dense data storage and low latency and high throughput. This thesis describes how to improve data storage, throughput, and latency performance for big data. E.Sivaraman Dr.R.Manickachezian(2014) said systems are becoming increasingly data-intensive due to the explosion of data and the need to process it, therefore ,the application performance is vital to storage management in data-intensive computing systems. The author mentioned above highligted that researchers believe that applications sensitive areas are advancing in storing large volume of data. As data volumes continue to grow, the movement of data from memory to storage systems has become a critical performance bottleneck for many data centers. However, data management on such multi-server storage systems is yet under studies to ascertain on how to utilize various storages for efficient data movement is an important research topic in RPC data perception. In this research, we propose an adoptive Heartbeat system, data-aware distribution that efficiently manages storage systems to coordinate multiple concurrent data-intensive applications. Extensive experiments are conducted and the results show that this method can significantly improve the network throughput performance and data-intensive storage of Hadoop in HDFS clusters. The thesis discusses the background of RPC, huge data application storage systems, related work and technologies, Hadoop installation, adaptive model mechanisms, evaluation and performance analysis of big data architecture and its server interfaces. The reason to focus on storage improvements is because of the great need to store information globally. A large class of communication-intensive distributed applications and software components have been ported to virtual machines, such as highperformance storage systems. This method dynamically adjusts the RPC configuration between the NameNode and the DataNode by sensing the data characters stored in the DataNode. This method can effectively reduce the processing pressure of the NameNode and improve the network throughput generated by the information transmission between the NameNode and the DataNode.
A large volume of data created on daily basics from modern information systems and latest digital... more A large volume of data created on daily basics from modern information systems and latest digital technologies such as cloud of computing and the user while handling huge dataset meets Internet of Things and difference challenges. Big Data referred to as a large data of single data set of related data as compared to separate tiny datasets with similar amount of data stored in it. Big Data can be a constantly rising from a few dozen terabytes to many petabytes ranging from data. Big data can also be termed as a combination of big and complex data sets that have the vast volume of data, social media analytics, data management efficiency, real-time data. Big Data has several dimensions such as volume, variety, velocity and veracity .Big data processing involves a method called Hadoop, which uses the Mapreduce paradigm to process the data. In this regards, big data analysis is a current trending area of research and development around the world with an aim of improving data storage demands. The basic objective of this paper is to explore the potential impact of Big Data; Data processing, and Big Data Applications and Technologies, challenges and Hadoop associated with it. As a result, this article provides a platform to explore big data at various phases. Furthermore, it extends new chances for scholars to develop the solution, based on the challenges and open research issues.
This Scientific paper focuses on employment of artificial neural network techniques to develop i... more This Scientific paper focuses on employment of artificial neural network techniques to develop in-network "intelligent computation" capability for wireless sensor networks to improve their functionality, utility and survival aspects. The main purpose is to introduce computational intelligence capability for the wireless sensor networks to become adaptive to changes within a variety of operational contexts and to exhibit intelligent behavior. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper.
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