A Low-Cost Energy-Efficient Raspberry Pi Cluster for Data Mining Algorithms

Saffran, João; García, Gabriel; Souza, Matheus; Penna, Pedro Henrique; Castro, Márcio; Góes, Luís F. W.; Freitas, Henrique C.

doi:10.1007/978-3-319-58943-5_63

Cited by 18 publications

(13 citation statements)

References 14 publications

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“…A Low-Cost Energy-Efficient Raspberry Pi Cluster for Data Mining Algorithms [6] Raspberry Pi 2 Intel Xeon Phi Data mining algorithms: Association Rule Learning (A priori) and K-Means.…”

Section: Spark Applications Benchmark (Microbenchmarks)mentioning

confidence: 99%

Evaluation of Clusters based on Systems on a Chip for High-Performance Computing: A Review

Aldana¹,

Buitrago²,

Gutiérrez³

2020

rev.ing.univ.Medellin

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High-performance computing systems are the maximum expression in the field of processing for large amounts of data. However, their energy consumption is an aspect of great importance, which was not considered decades ago. Hence, software developers and hardware providers are obligated to approach new challenges to address energy consumption, and costs. Constructing a computational cluster with a large amount of systems on a chip can result in a powerful, ecologic platform, with the capacity to offer sufficient performance for different applications, as long as low costs and minimum energy consumption can be maintained. As a result, energy efficient hardware has an opportunity to impact upon the area of high-performance computing. This article presents a systematic review of the evaluations conducted on clusters of ystems on a Chip for High-Performance computing in the research setting.

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“…A Low-Cost Energy-Efficient Raspberry Pi Cluster for Data Mining Algorithms [6] Raspberry Pi 2 Intel Xeon Phi Data mining algorithms: Association Rule Learning (A priori) and K-Means.…”

Section: Spark Applications Benchmark (Microbenchmarks)mentioning

confidence: 99%

Evaluation of Clusters based on Systems on a Chip for High-Performance Computing: A Review

Aldana¹,

Buitrago²,

Gutiérrez³

2020

rev.ing.univ.Medellin

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“…Kruger [44] demonstrated that a cluster of Parallella nodes can compete in terms of performance with an Intel i5-3570 server. Similarly, Saffran et al [45] analysed performance and energy consumption of a cluster composed of cost-efficient nodes with a co-processor designed specifically for HPC while executing two Big-Data data-mining algorithms. In [46], the role of SBC-based clusters in energy efficient data centers in the context of big data applications was studied.…”

Section: Single-board Computers As Micro Data-center Serversmentioning

confidence: 99%

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

Fernández-Cerero

Fernández-Rodríguez

Álvarez-García

et al. 2019

Sensors

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The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.

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“…Several open source microscope designs have been proposed using 3D printing and low cost computing platforms like the Raspberry Pi [7,8,17]. Starting as low as 5$ for a fully featured computer capable of running a desktop version of Linux, the Raspberry Pi allows scientists to use dedicated clusters of computers in virtually any application [18,19,20].…”

Section: Introductionmentioning

confidence: 99%