This presentation demonstrates a performance study of Hadoop MapReduce based on ARM cluster. It compared MapReduce applications performance and energy consumption between ARM cluster and general x86_64 cluster.

1. Hadoop MapReduce Performance
Study on ARM cluster
Yanjun Wang
yanjun@cse.unl.edu

2. Outlines
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Motivation
Introduction
Evaluation
Conclusion
Questions

3. Motivation
• A credit card size Raspberry Pi can run general
Linux with very low power consumption

4. Motivation
• ARM cluster vs. x86_64 cluster

5. Introduction
• Hadoop MapReduce
• Cubieboard2

6. Evaluation
• Environment
– ARM cluster: 4 cubieboard2, 1 head node, 3 worker
nodes; lubuntu for ARM, java-1.7 for ARM
– X86_64 cluster: 2 firefly nodes. 1 head node, 1 worker
node; CentOS 6.3, java-1.7
• Hadoop 1.2 [1]
• Testcases
– Loadgen
– MDAD (Molecular Dynamics Simulation based on
Hadoop MapReduce [2])
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[1]Apache Hadoop
[2]Chen He, “Molecular Dynamics Simulation based on Hadoop MapReduce”
, Master thesis, 2011

7. Evaluation
• Equivalent Performance
– Run program on current device and get
turnaround time
– To achive the same turnaround time, how many
new devices we need, or could we this?
• Energy consumption
– Kill-a-Watt device to collect ARM cluster energy;
– ServerTech PDU for gathering x86_64 cluster
energy consumption

8. Evaluation
• loadgen

9. Evaluation
• MDAD

10. Evaluation
• Loadgen Energy

11. Evaluation
• MDAD energy

12. Conclusion
• We build a ARM cluster which is composed of
4 cubieboard2 cards.
• We setup Hadoop cluster on the ARM cluster
• We compared the performance and energy
consumption between two clusters
• Based on our current data, we conclude that
ARM cluster is not an alternative choice to
replace X86_64.