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Raspberry Pi units sold (all versions), according to statistics published by the official Raspberry Pi blog.
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![Fig. 2. A selection of SBC clusters. (a) Iridis Pi [3] built using...](publication/326085229/figure/fig2/AS:864406321758213@1583101935653/A-selection-of-SBC-clusters-a-Iridis-Pi-3-built-using-Lego-b-Mythic-Beasts-21_Q320.jpg)
Current commodity Single Board Computers (SBCs) are sufficiently powerful to run mainstream operating systems and workloads. Many of these boards may be linked together, to create small, low-cost clusters that replicate some features of
large data center clusters. The Raspberry Pi Foundation produces a series of
SBCs with a price/performance ratio...
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... were other SBCs, such as the Gumstix [1] and the BeagleBone, that had similar technical specifications (although they were more expensive) [8]. Despite this, it is the Raspberry Pi that has come to lead the market. Selling over a million units in the first year, the Raspberry Pi Foundation became the fastest growing computing company to date [2]. Fig. 1 shows the sales figures for Raspberry Pi units, based on statistics published by the official Raspberry Pi blog, and in March 2017 the Raspberry Pi became the third best-selling general purpose computer of all time [12]. From the early days of the Linux capable SBC, when the list of available boards was extremely limited, there is now ...
Citations
... The brain of the ICA platform is a single-board computer (SBC). An SBC consists of the central processing unit (CPU), random access memory (RAM), solid-state storage (SSS), and peripheral ports combined into a small form factor printed circuit board (PCB) [23][24][25][26][27]. The main two candidates for SBC were Nvidia Jetson Nano 4GB [28][29][30][31] and Raspberry Pi 4 Model B 8GB [32][33][34][35]. ...
Intelligent compaction (IC) is a technology that uses non-contact sensors to monitor and record the compaction level of geomaterials in real-time during road construction. However, current IC devices have several limitations: (i) they are unable to visualize or compare multiple intelligent compaction measurement values (ICMVs) in real-time during compaction; (ii) they are not retrofittable to different conventional rollers that exist in the field; (iii) they do not incorporate corrections for ICMVs reflecting variable field conditions; (iv) they are unable to integrate construction specifications as needed for performance-based compaction; and (v) they do not record all the key roller parameters for further compaction analysis. To address these issues, an innovative retrofittable platform with cutting-edge hardware and software was developed. This platform, called the intelligent compaction analyzer (ICA) platform, is effective at calculating conventional acceleration amplitude-based ICMVs and stiffness-based parameters and at displaying the spatial distributions of these parameters in a colour-coded map in real-time during compaction.
... Several studies have proposed the adoption of commodity Single-Board Computer (SBC) clusters as a promising alternative to conventional data centers. The authors in [2] sustain that the use of low consumption devices such as SBC's can be an option to minimize the infrastructure problems of data centers. A single board can encapsulate all the resources of a functional computer and with relatively good processing power that, when interconnected as a cluster, can replicate characteristics of large data centers. ...
The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, have been used to increase horizontal scalability and high availability of data store systems. In this paper, we evaluated three distributed databases on a low-power low-cost cluster of commodity Single-Board Computers (SBC): relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase. The cluster has 15 Raspberry Pi 3 nodes with Docker Swarm orchestration tool for service deployment and ingress load balancing over SBCs. We believe that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity, and high availability. Experimental results clearly demonstrated that there is a trade-off between performance and replication, which provides availability and partition tolerance. Besides, both properties are essential in the context of distributed systems with low-power boards. Cassandra attained better results with its consistency levels specified by the client. Both Citus and HBase enable consistency but it penalizes performance as the number of replicas increases.
... The brain of the ICA platform is a single-board computer (SBC). An SBC consists of the central processing unit (CPU), random access memory (RAM), solid-state storage (SSS) and peripheral ports combined into a small form factor printed circuit board (PCB) 29,30,31,32,33 . The main two candidates for SBC were Nvidia Jetson Nano 4GB 34,35,36,37 and Raspberry Pi 4 Model B 8GB 38,39,40,41 . ...
This study aims to develop an innovative retrofittable platform with cutting-edge hardware and software tools to advance the current state of intelligent compaction technology. We built a prototype of this platform from the ground up, hereafter referred to as the Intelligent Compaction Analyser (ICA) platform. This platform is designed to provide the following novel features: [i] simultaneous visualisation of multiple intelligent compaction measurement values (ICMVs) in real-time during compaction; [ii] versatility to retrofit to an existing conventional roller; [iii] ability to incorporate corrections for different ICMV parameters reflecting variable field conditions; [iv] customization of construction specifications as needed for performance-based compaction; [v] ability to record key roller parameters such as speed, vibration frequency and amplitude for further analysis; and (vi) capability to monitor the raw vibration signal pattern and its frequency spectrum. In this paper, we provide the basic design concepts of this platform, outline its functionalities and capabilities, and provide initial field validation results. The results show that the ICA platform is effective in calculating conventional acceleration amplitude-based ICMVs and stiffness-based parameters, and in displaying the spatial distributions of these parameters in a colour-coded map in real-time during compaction.
... With the emergence of ARM-based SBC [13], especially Raspberry Pi offering multiple cores, a large number of small-scale clusters have shown up on the landscape mostly for demonstration and educational purposes. In the literature survey, several papers were found using Raspberry Pi for implementing HPC clusters for many different applications. ...
HPC systems are ubiquitous yet inaccessible to college/university students as most universities in India still lack the infrastructure, experience, and budget for setting up an HPC facility. Therefore, it is highly desirable for academia to use effective pedagogy to train students about HPC concepts and develop skills to write parallel applications. In this paper, we present the hands-on approach of learning cluster architecture and the development of applications that can interact with HPC systems in a Parallel and Distributed Computing (PDC) environment. The applications have been developed on a testbed cluster and successfully scaled on the Institute for Plasma Research (IPR) Petascale cluster named ANTYA. These applications have been used as a tool for HPC outreach activities at IPR for training young college students about PDC along with a hands-on understanding of cluster architecture by building testbeds from scratch.
... The single-board computer (SBC), a rather powerful type of machine that can be used as a generic IoT device, enjoys enormous popularity due to their high performance for their price range and the vast number of settings wherin they can be used. These devices are becoming a standard for IoT prototyping and implementation [6][7][8], bringing more and more computing power to this domain and also more underutilized devices. ...
... For the implementation of the framework, we used Elixir programming language [29] because of the ease it provides to distribute and execute data and code by devices in a network. For the hardware, we used Raspberry Pis, a type of SBC (single-board computer) that can be used as a generic IoT device and enjoys enormous popularity due to is high performance for its price range and the vast number of scenarios where it can be used [6][7][8]. The use of this type-o SBC also allows the use of Linux as the operating system to implement a resource reservation policy and use Elixir along with the Erlang virtual machine [30] in each one of them, creating a distributed scenario that is simpler to manage. ...
In this paper, we present a framework for exploring the spare capacity of IoT devices for clustered execution of multimedia applications. Applications of this type are usually framed with specific quality parameters that enable a desirable level of service. This means that the IoT cluster must guarantee strict quality ranges of service to work as expected. The framework is totally customizable, and QoS dimensions can be easily added or removed given their relevance in the application scenario. The achieved results clearly demonstrate the utility of using the spare capacity of IoT devices, otherwise unused, to cooperatively execute servies within the desired quality of service levels.
... We limit the computational and network resources of the container executing the Follower agent to reproduce a scenario involving resource-constrained and battery-powered devices. Reference devices are single-board computers (SBC) and micro-controller units (MCU) [12,18,27]; thus, container resources are upper-bounded at 5% of one CPU core, 20 MB of RAM, and 1 Mbps of bandwidth. ...
Monitoring is a critical component in fog environments: it promptly provides insights about the behavior of systems, reveals Service Level Agreements (SLAs) violations, enables the autonomous orchestration of services and platforms, calls for the intervention of operators, and triggers self-healing actions. In such environments, monitoring solutions have to cope with the heterogeneity of the devices and platforms present in the Fog, the limited resources available at the edge of the network, and the high dynamism of the whole Cloud-to-Thing continuum. This paper addresses the challenge of accurately and efficiently monitoring the Fog with a self-adaptive peer-to-peer (P2P) monitoring solution that can opportunistically adjust its behavior according to the collected data exploiting a lightweight rule-based expert system. Empirical results show that adaptation can improve monitoring accuracy, while reducing network and power consumption at the cost of higher memory consumption.
... Pemrosesan berkelanjutan dan tidak pernah berakhir dapat menyebabkan penurunan total dalam kinerja pemrosesan enkripsi. Selain itu, dapat menyebabkan kerusakan pada perangkat komputer yang digunakan untuk memproses enkripsi [16][17] [18]. ...
Data security is still a major issue regarding the need for data confidentiality. The encryption process using the RSA algorithm is still the most popular method used in securing data because the complexity of the mathematical equations used in this algorithm makes it difficult to hack. However, the complexity of the RSA algorithm is still a major problem that hinders its application in a more complex application. Optimization is needed in the processing of this RSA algorithm, one of which is by running it on a distributed system. In this paper, we propose an approach with a FIFO process scheduling algorithm that runs on a single board computer cluster. The test results show that the allocation of resources in a system that uses a FIFO process scheduling algorithm is more efficient and shows a decrease in the overall processing time of RSA encryption
... Authors in [4] are having a closer look at the reasons behind the growth of SBC clusters. Indeed, SBC clusters have reduced footprint, have less energy requirements, and are cheaper which gives computational game changing results. ...
Increases in power demand and consumption are very noticeable. This increase presents a number of challenges to the traditional grid systems. Thus, there is the need to come up with a new solution that copes with the stringent demand on energy and provides better power quality, which gives a better experience to the end users. This is how the concept of smart grids (SG) came to light. SGs have been introduced to better monitor and control the power produced and consumed. In addition to this, SGs help with reducing the electricity bill through the integration of renewable energy sources. The underlying smartness of the SGs resides in the flow of information in addition to the flow of energy. Information/data flowing implies the use of smart sensors and smart meters that sense and send data about the power produced and consumed, and the data about the environment where they are deployed. This makes SGs a direct application of IoT. In this paper, we are implementing an edge platform that is based on single-board computers (SBCs) to process data stemming from SG. The use of SBCs is driven by the energy efficiency and cost effectiveness concepts that the SG is trying to apply. The platform in question is tested against a distributed job that averages random numbers using Hadoop’s MapReduce programming model. The SBC that we are using in this implementation is the NVIDIA Jetson Developer Kit. The results of this work show that a cluster of SBCs is low-cost, easy to maintain, and simple to deploy, which makes it a great candidate for providing edge computing. Although it revealed a performance that beat the one of the remote cloud servers, it could not outperform the single-computer edge platform.
... These changes have increased the adoption rate of SBCs as edge nodes for FC and EC, either as standalone devices or in clusters. This is evident by the variety of scenarios that utilize these devices: low-latency cyber-physical systems, resource-constrained computing, and next-generation data centers [8]. ...
... In the last years, the applicability of SBCs has increased and now covers a wide range of use cases. In [8], Johnston et al. perform an in-depth analysis of the state-ofthe-art use cases for these devices. They explain the main characteristics of SBCs and detail the different device models. ...
... The purpose of the following steps in this algorithm is to define the potential candidate SBCs where virtual nodes can be placed. Each physical node with a battery percentage above a minimum predefined value and a scheduled status is added to l candidate (lines [6][7][8]. ...
The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.
... The single-board computer consumes energy even when it is in an idle state because it typically runs an operating system (OS) [40]. The Raspberry Pi, which we used as the single-board computer to implement the low-power image-based sensor, also runs the Linuxbased Raspbian OS. ...
Occupancy lighting control, which is used to turn lights on/off based on the occupancy state measured by an occupancy sensor, is a popular and effective type of automatic lighting control. This paper clarifies the effectiveness of occupancy control based on a low-power image-based sensor, which measures only the occupancy state by applying an image processing technique to visible images, implemented on a low-cost single-board computer. We found that the rated power consumption of the low-power image-based sensor was 69.50% or less than that of commercial image-based sensors. In addition, the low-power image-based sensor increased the total comprehensive energy-saving rate of the occupancy control by 5.38% or more compared with a commercial PIR sensor and commercial image-based sensor. Further analysis of the effectiveness of the low-power image-based sensor is provided herein.
