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Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data

Hajji, W and Tso, FP (2016) Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data. Electronics, 5 (2). ISSN 2079-9292

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Abstract

Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume.
Often the data require real-time processing to support high system responsiveness which can be
supported by localised Cloud and/or Fog computing paradigms. However, there are considerably
large deployments of IoT such as sensor networks in remote areas where Internet connectivity is
sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the
Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct
low-cost, low-power portable cloud to support real-time data processing next to IoT deployments.
In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its
feasibility for real-time big data analytics under realistic application-level workload in both native
and virtualised environments. We have extensively tested the performance of a single node Raspberry
Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed
File System). Our results have demonstrated that our portable cloud is useful for supporting real-time
big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound
workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data
applications, the virtualisation overhead is fractional for small jobs but becomes more significant for
large jobs, up to 28.6%.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: MDPI
Related URLs:
Date Deposited: 15 Jun 2016 08:09
Last Modified: 07 Sep 2017 09:52
DOI or Identification number: 10.3390/electronics5020029
URI: http://researchonline.ljmu.ac.uk/id/eprint/3778

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