Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data
AbstractNowadays, 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%. View Full-Text
A printed edition of this Special Issue is available here.
Share & Cite This Article
Hajji, W.; Tso, F.P. Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data. Electronics 2016, 5, 29.
Hajji W, Tso FP. Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data. Electronics. 2016; 5(2):29.Chicago/Turabian Style
Hajji, Wajdi; Tso, Fung P. 2016. "Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data." Electronics 5, no. 2: 29.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.