Hadoop Performance Analysis on Raspberry Pi for DNA Sequence Alignment

Authors: Jaya Sena Turana, Heru Sukoco, Wisnu Ananta Kusuma Journal: TELKOMNIKA, Vol. 14 No. 3 (2016) DOI: 10.12928/TELKOMNIKA.v14i3.1886

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Abstract

The rapid development of electronic data has created challenges in storing and processing big data. Hadoop provides a distributed computing framework that can run on commodity hardware. This research analyzes the performance of a Hadoop cluster built using six Raspberry Pi Model B devices for DNA sequence alignment using the Biodoop library.

Key results:

• Average CPU usage: 73.08%
• Average memory usage: 334.69 MB
• Average job completion time: 19.89 minutes
• Block distribution reduced CPU usage by 24.14%
• Block distribution reduced memory usage by 8.49%
• Processing time increased by 31.53%

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1. Introduction

Big Data is characterized by:

• Volume
• Variety
• Velocity

Hadoop provides:

• HDFS (Hadoop Distributed File System)
• MapReduce processing framework

Raspberry Pi was selected because:

• Low cost
• Low power consumption
• Easy cluster deployment
• Suitable for educational and research environments

The objective of this research is to analyze Hadoop cluster performance on Raspberry Pi for DNA sequence alignment.

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2. Related Works

Several Raspberry Pi clusters have previously been developed:

These studies demonstrated the feasibility of low-cost distributed computing.

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3. Research Method

Hardware

• 6 Raspberry Pi Model B
• 1 NameNode
• 5 DataNodes
• Raspbian OS

Software

• Apache Hadoop
• HDFS
• MapReduce
• Biodoop
• Ganglia Monitoring

DNA Datasets

Biodoop Workflow

1. Upload FASTA file into HDFS
2. Convert FASTA to TAB format using fasta2tab
3. Execute BLAST MapReduce job using biodoop_blast
4. Analyze alignment results

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4. Hadoop Configuration

Heap Size Experiment

Optimal value:

HADOOP_HEAPSIZE = 384 MB

Additional Configuration

dfs.client.file-block-storage-locations.timeout = 1200
dfs.namenode.fs-limits.min-block-size = 512

Custom block sizes used:

3 KB
5 KB
10 KB

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5. DNA Sequence Alignment Results

Test 1

Reference:

Ancylostoma duodenale mitochondrion

Query:

Necator americanus mitochondrion

Results:

• Similarity > 89%
• Bit Score = 1225

Raspberry Pi Performance

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Test 2

Reference:

Human papillomavirus type 132

Query:

Human papillomavirus type 134

Results:

• Similarity > 93%
• Bit Score = 43.5

Raspberry Pi Performance

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Test 3

Reference:

Chaetoceros tenuissimus DNA virus

Query:

Chaetoceros lorenzianus DNA Virus

Results:

• Similarity > 83%
• Bit Score = 63

Raspberry Pi Performance

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6. Analysis

Effects of Hadoop block distribution:

Advantages

• CPU usage reduced by 24.14%
• Memory usage reduced by 8.48%
• Better workload distribution across DataNodes

Disadvantages

• Processing time increased by 31.53%
• Additional MapReduce overhead
• Additional block-splitting overhead

For small DNA files, Hadoop overhead becomes significant relative to computation time.

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7. Conclusion

Raspberry Pi can successfully be used as a low-cost Hadoop cluster platform for bioinformatics workloads.

Main findings:

• Hadoop runs reliably on Raspberry Pi.
• DNA sequence alignment using Biodoop was successfully executed.
• Average CPU utilization reached 73.08%.
• Average memory utilization reached 334.69 MB.
• Average completion time was 19.89 minutes.
• Block distribution reduced CPU and memory usage.
• Processing time increased because of MapReduce overhead.

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Future Work

Future research may include:

1. Hadoop Native ARM Library
2. Newer Raspberry Pi hardware
3. Larger DNA datasets
4. Spark-based implementation
5. Energy efficiency analysis

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Citation

Turana, J. S., Sukoco, H., & Kusuma, W. A. (2016). Hadoop Performance Analysis on Raspberry Pi for DNA Sequence Alignment. TELKOMNIKA, 14(3), 1059–1066.