NDLI: Apache Hadoop Yarn Parameter configuration Challenges and Optimization

Content Provider	IEEE Xplore Digital Library
Author	Mathiya, B.J. Desai, V.L.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Gov. Sci. Coll., Navsari, India (Desai, V.L.) \|\| C.U. Shah Univ., Wadhwan, India (Mathiya, B.J.)
Abstract	Apache Hadoop Yarn is an open source framework for distributed as well as local storage, processing and analysis of big data on commodity hardware. It provides MapReduce Programming Model, HDFS for Distributed File System and Default parameter configuration settings. MapReduce Programming Model provides Mapper and Reducer interface function for parallel computing, processing and execution of program. HDFS provides file system for storing data locally and distributed. Apache Hadoop provides more than hundreds default parameter configuration settings common for all type of clusters and applications. Apache Hadoop Yarn provides functionality that user can customize parameter configuration settings as per their needs through xml file setting as well as in writing program coding for performance tuning of resources likes CPU, I/O, Memory, and Network. Customizing parameter configuration is a black art which required good knowledge of each parameter that what is impact when we change its default values because all parameter are interconnected and affected each other performance. Proper parameter configuration can improve and tune performance as well as misparameter configuration setting can decrease performance of the system. It is challenge that performance tuning of Apache Hadoop Framework through balanced customize parameter configuration setting that it cannot over utilize or under utilize system resources. In this paper we study and analysis of difference type of research paper related to customizing parameter configurations setting for performance tuning of Apache Hadoop jobs and better utilization of available resources. We found that good customization of parameter configuration improve performance compare to default parameter setting.
Starting Page	1
Ending Page	6
File Size	236440
Page Count	6
File Format	PDF
ISBN	9781479917525
e-ISBN	9781479917532
DOI	10.1109/ICSNS.2015.7292373
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-02-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	File systems Apache Hadoop Yarn Parameter Configuration Memory management Distributed databases Hadoop Hardware Yarn Tuning Optimization MapReduce
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Performance evaluation and tuning for MapReduce computing in Hadoop distributed file system

JellyFish: Online Performance Tuning with Adaptive Configuration and Elastic Container in Hadoop Yarn

Hadoop: Addressing challenges of Big Data

Study of Hadoop-MapReduce on Google N-Gram Datasets

Big data: Issues, challenges, tools and Good practices

New Spam Filtering Method with Hadoop Tuning-Based MapReduce Naïve Bayes

Designing Customized Framework for Performance Tuning in Hadoop

The impact of cluster characteristics on HiveQL query optimization

A MapReduce Optimization Method on Hadoop Cluster

Apache Hadoop Yarn Parameter configuration Challenges and Optimization

Similar Documents

Performance evaluation and tuning for MapReduce computing in Hadoop distributed file system

JellyFish: Online Performance Tuning with Adaptive Configuration and Elastic Container in Hadoop Yarn

Hadoop: Addressing challenges of Big Data

Study of Hadoop-MapReduce on Google N-Gram Datasets

Big data: Issues, challenges, tools and Good practices

New Spam Filtering Method with Hadoop Tuning-Based MapReduce Naïve Bayes

Designing Customized Framework for Performance Tuning in Hadoop

The impact of cluster characteristics on HiveQL query optimization

A MapReduce Optimization Method on Hadoop Cluster

Apache Hadoop Yarn Parameter configuration Challenges and Optimization