High performance analytics of bigdata with dynamic and optimized hadoop cluster

Pradhananga, Yanish; Karande, Shridevi; Chandraprakash, Karande

doi:10.1109/icaccct.2016.7831733

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…Hadoop Single Node Cluster (SNC) [13] is non distributed or standalone mode with single java instance runs for all daemons in one node. The Hadoop Multi Node Cluster (MNC) [14] can have more than one DataNodes.…”

Section: A Single Node Cluster (Snc)mentioning

confidence: 99%

Designing Authentication for Hadoop Cluster using DNA Algorithm.

Balaraju*¹,

Rao²

2019

IJRTE

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Big Data (BD) generation are exponentially increased and it is necessarily required for modern society. Hadoop Clusters (HC) provides the facilities like Processing, storage and doesn’t have built-in security. But this feature is important as it increases the analysis speed, storage process. HC facilitates storage, processing of data, on the other hand processing of streaming data handled by the Apache Spark. However data storage, processing power, cluster management and data security in HC is not reached up to the mark with increased data. In such situations, HC are scaled out from small scale IT organization and it depends on public cloud centers with lack of data security, communication, computation and operational cost. On the other hand data security in HC is major issue and it uses a separat security mechanisms. This paper proposes New Algorithm Built in Authentication Based on Access (BABA) as a security instance integrated as Hadoop instance for securing data in HC from attackers along with metadata security for avoiding crashes Hadoop. This mechanism provides a secured HC without using other security configurations which will reduce operational cost, computational power, increases data security and providing a better solution for HC.

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Section: A Single Node Cluster (Snc)mentioning

confidence: 99%

Designing Authentication for Hadoop Cluster using DNA Algorithm.

Balaraju*¹,

Rao²

2019

IJRTE

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“…3 that in both servers (P1 and P2), the execution time increases with an increase in the size of the dataset. Obviously, there are challenges with system performance when processing big data as observed in [33][34][35]. This has led to different proposals and implementations of techniques which can improve the performance of systems that process big data.…”

Section: Performance Evaluation Of Contramentioning

confidence: 99%

Mining and visualising contradictory data

2017

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A noisy dataset can contain contradictory data. Contradictory data is synonymous to incorrect data and it is important that such data be investigated and evaluated when analysing a noisy dataset. Different approaches to dealing with contradictory data have been proposed by different researchers. For example [1, 2] proposed methods for identifying and removing contradictory data in noisy datasets. However, the removal of contradictory data from a noisy dataset will increase the incompleteness in the dataset thereby reducing the soundness of any information from such set of data. It is therefore important to identify and evaluate contradictory instances when analysing a large and noisy dataset. This will improve the soundness of the analysis from such a dataset. Evidently, the analysis of big data is identified as the next frontier for innovation and advancement of technology [3, 4]. There is therefore the need to identify appropriate approaches to dealing with contradictions in a large and noisy dataset. There are different forms of contradictions. For example, there are contradictions from the use of modal words, structural, subtle lexical contrasts, as well as world knowledge

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“…The services which are deployed in cloud interact or communicate with the services in private cloud using APIs. By deploying services which required high computation resource in cloud give flexibility to perform bigdata analytics in desire time by allocating resources dynamically [9].…”

Section: Third Tiermentioning

confidence: 99%

Tiarrah Computing: The Next Generation of Computing

Pradhananga

Rajarajeswari

2017

IJ-ICT

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The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge.

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High performance analytics of bigdata with dynamic and optimized hadoop cluster

Cited by 12 publications

References 8 publications

Designing Authentication for Hadoop Cluster using DNA Algorithm.

Designing Authentication for Hadoop Cluster using DNA Algorithm.

Mining and visualising contradictory data

Tiarrah Computing: The Next Generation of Computing

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