학술논문

Performance Evaluation and Estimation Model Using Regression Method for Hadoop WordCount
Document Type
Periodical
Author
Source
IEEE Access Access, IEEE. 3:2784-2793 2015
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Cloud computing
Estimation
Computational modeling
Analytical models
Computer architecture
Hardware
Benchmark testing
performance analysis
cloud computing
doop WordCount
Language
ISSN
2169-3536
Abstract
Given the rapid growth in cloud computing, it is important to analyze the performance of different Hadoop MapReduce applications and to understand the performance bottleneck in a cloud cluster that contributes to higher or lower performance. It is also important to analyze the underlying hardware in cloud cluster servers to enable the optimization of software and hardware to achieve the maximum performance possible. Hadoop is based on MapReduce, which is one of the most popular programming models for big data analysis in a parallel computing environment. In this paper, we present a detailed performance analysis, characterization, and evaluation of Hadoop MapReduce WordCount application. We also propose an estimation model based on Amdahl’s law regression method to estimate performance and total processing time versus different input sizes for a given processor architecture. The estimation regression model is verified to estimate performance and run time with an error margin of