Journal Article10.1016/J.FUTURE.2011.05.007
HSim: A MapReduce simulator in enabling Cloud Computing
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TL;DR: HSim, a MapReduce simulator which builds on top of Hadoop, models a large number of parameters that can affect the behaviors of Map Reduce nodes, and thus it can be used to tune the performance of a Mapreduce cluster.
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About: This article is published in Future Generation Computer Systems. The article was published on 01 Jan 2013. The article focuses on the topics: Cloud computing & Benchmark (computing).
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Citations
A Descriptive Literature Review and Classification of Cloud Computing Research
Haibo Yang,Mary Tate +1 more
TL;DR: In this article, the authors present a descriptive literature review and classification scheme for cloud computing research, which includes 205 refereed journal articles published since the inception of cloud computing and classify them into four main categories: technological issues, business issues, domains and applications, and conceptualizing cloud computing.
A Descriptive Literature Review and Classification of Cloud Computing Research
Haibo Yang,Mary Tate +1 more
TL;DR: A descriptive literature review and classification scheme for cloud computing research is presented, showing that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging.
115
A Survey on Automatic Parameter Tuning for Big Data Processing Systems
TL;DR: This work investigates existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation- based, experiment-driven, machine learning, and adaptive tuning.
92
MapReduce and Its Applications, Challenges, and Architecture: a Comprehensive Review and Directions for Future Research
TL;DR: This paper provides a discussion of the differences between varied implementations of MapReduce as well as some guidelines for planning future research.
72
Cooperation of simulation and data model for performance analysis of complex systems
Byeong-soo Kim,Tag Gon Kim +1 more
TL;DR: This paper identifies the characteristics of each modelling method and presents a cooperative model development process for performance analysis of complex systems and applies the proposed modelling to develop a model of Hadoop using artificial neural network and discrete event systems specification.
References
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
- 06 Dec 2004
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
A simulation approach to evaluating design decisions in MapReduce setups
Guanying Wang,Ali R. Butt,Prashant Pandey,Karan Gupta +3 more
- 28 Dec 2009
TL;DR: The resulting simulator, MRPerf, captures such aspects of MapReduce setups as node, rack and network configurations, disk parameters and performance, data layout and application I/O characteristics, among others, and uses this information to predict expected application performance and can serve as a tool for optimizing existing MapReduces setups as well as designing new ones.
257
•Book
Pro Hadoop
Jason Venner
- 17 Jun 2009
TL;DR: The ins and outs of MapReduce; how to structure a cluster, design, and implement the Hadoop file system; and how to build your first cloudcomputing tasks using Hadooper, from Apress.
138
Using realistic simulation for performance analysis of mapreduce setups
Guanying Wang,Ali R. Butt,Prashant Pandey,Karan Gupta +3 more
- 10 Jun 2009
TL;DR: The design of an accurate MapReduce simulator, MRPerf, is presented, which can serve as a design tool for Map Reduce infrastructure, and as a planning tool for making Map reduce deployment far easier via reduction in the number of parameters that currently have to be hand-tuned using rules of thumb.