NDLI: Towards Deploying Elastic Hadoop in the Cloud

Content Provider	IEEE Xplore Digital Library
Author	Hong Mao Zhenzhong Zhang Bin Zhao Limin Xiao Li Ruan
Copyright Year	2011
Abstract	The fast development of internet application is boosting the development of cloud computing, a new paradigm of provisioning computing infrastructure and services over network. In cloud computing environment, MapReduce is often used to perform scientific computing like matrix multiplication and do data mining and information extraction on massive data. Hadoop, an open-source implementation of MapReduce, is a suitable tool to parallelly deal with these kinds of applications. While current hadoop environments are mainly deployed on physical servers manually and are lack of flexibility. This paper proposes the EHAD (Elastic Hadoop Auto-Deployer) system to creates/destroys corresponding number of VM nodes and deploys/releases hadoop environment among the VM nodes for client users in service level. We also propose multithreading and VMOP (Virtual Machine Optimized Placement) to improve the service quality of EHAD. Experiments show that our EHAD system can deploy a hadoop cluster on demand in less than 300 seconds. The multithread method could shorten the time consumption of creating 28 VMs by 3 times and VMOP policy could improve the runtime performance of hadoop cluster by 9.73 percent.
Starting Page	476
Ending Page	482
File Size	297020
Page Count	7
File Format	PDF
ISBN	9781457718274
DOI	10.1109/CyberC.2011.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing Multithreading Databases Elastic Instruction sets Cloud Environment Hadoop Deployment Virtual machining Servers Time factors MapReduce
Content Type	Text
Resource Type	Article

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