On Energy Efficiency and Performance Evaluation of Single Board Computer Based Clusters: A Hadoop Case Study
1
Robotics and IoT Lab, Department of Computer Science, Prince Sultan University, Riyadh 11586, Saudi Arabia
2
CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(2), 182; https://doi.org/10.3390/electronics8020182
Received: 30 January 2019 / Revised: 31 January 2019 / Accepted: 31 January 2019 / Published: 4 February 2019
(This article belongs to the Special Issue Challenges and Opportunities of IoT Deployments—Avoiding the Internet of Junk)
Energy efficiency in a data center is a challenge and has garnered researchers interest. In this study, we addressed the energy efficiency issue of a small scale data center by utilizing Single Board Computer (SBC)-based clusters. A compact layout was designed to build two clusters using 20 nodes each. Extensive testing was carried out to analyze the performance of these clusters using popular performance benchmarks for task execution time, memory/storage utilization, network throughput and energy consumption. Further, we investigated the cost of operating SBC-based clusters by correlating energy utilization for the execution time of various benchmarks using workloads of different sizes. Results show that, although the low-cost benefit of a cluster built with ARM-based SBCs is desirable, these clusters yield low comparable performance and energy efficiency due to limited onboard capabilities. It is possible to tweak Hadoop configuration parameters for an ARM-based SBC cluster to efficiently utilize resources. We present a discussion on the effectiveness of the SBC-based clusters as a testbed for inexpensive and green cloud computing research.
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Keywords:
green cloud computing; ARM32 single board computers; Hadoop MapReduce; power consumption; performance evaluation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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Externally hosted supplementary file 1
Doi: 10.5281/zenodo.2553062
Link: https://zenodo.org/badge/DOI/10.5281/zenodo.2553063.svg
Description: Raw Datasets for Hadoop performance benchmarks on the three clusters
MDPI and ACS Style
Qureshi, B.; Koubaa, A. On Energy Efficiency and Performance Evaluation of Single Board Computer Based Clusters: A Hadoop Case Study. Electronics 2019, 8, 182.
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