scholarly journals High Availability Server Using Raspberry Pi 4 Cluster and Docker Swarm

2021 ◽  
Vol 6 (1) ◽  
pp. 43-51
Author(s):  
T Yudi Hadiwandra ◽  
Feri Candra
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Low Cost ◽  
Web Server ◽  
Performance Testing ◽  
High Availability ◽  
Cluster System ◽  
Raspberry Pi ◽  
Server Infrastructure ◽  
Server System

In the Industrial 4.0 era, almost all activities and transactions are carried out via the internet, which basically uses web technology. For this reason, it is absolutely necessary to have a high-performance web server infrastructure capable of serving all the activities and transactions required by users without any constraints. This research aims to design a high-performance (high availability) web server infrastructure with low cost (low cost) and energy efficiency. low power) using Cluster Computing technology on the Raspberry Pi Single Board Computing and Docker Container technology. The cluster system is built using five raspberry Pi type 4B modules as cluster nodes, and the Web server system is built using docker container virtualization technology. Meanwhile, cluster management uses Docker Swarm technology. Performance testing (Quality of Service) of the cluster system is done by simulating a number of loads (requests) and measuring the response of the system based on the parameters of Throughput and Delay (latency). The test results show that the Raspberry Pi Cluster system using Docker Swarm can be used to build a High Availability Server system that is able to handle very high requests that reach Throughput = 161,812,298 requests / sec with an Error rate = 0%.

Dispatcher Based Dynamic Load Balancing on Web Server System

2012 ◽  
Vol 1 (2) ◽  
pp. 15-27
Author(s):  
Harikesh Singh ◽  
Shishir Kumar
Keyword(s):  
Load Balancing ◽  
High Speed ◽  
Low Cost ◽  
Web Server ◽  
High Availability ◽  
Dynamic Load Balancing ◽  
Computing Power ◽  
High Speed Networks ◽  
Server Systems ◽  
Server System

The traffic increasing in the network creates bulk congestion while the bulk transfer of data evolves. Performance evaluation and high availability of servers are important factors to resolve this problem using various cluster based systems. There are several low-cost servers using the load sharing cluster system which are connected to high speed networks, and apply load balancing technique between servers. It offers high computing power and high availability. A distributed website server can provide scalability and flexibility to manage with emergent client demands. Efficiency of a replicated web server system will depend on the way of distributed incoming requests among these replicas. A distributed Web-server architectures schedule client requests among the multiple server nodes in a user-transparent way that affects the scalability and availability. The aim of this paper is the development of a load balancing techniques on distributed Web-server systems.

GRID-ENABLED NON-RIGID REGISTRATION OF MEDICAL IMAGES

2004 ◽  
Vol 14 (02) ◽  
pp. 197-216 ◽  
Author(s):  
RADU STEFANESCU ◽  
XAVIER PENNEC ◽  
NICHOLAS AYACHE
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Parallel Implementation ◽  
Low Cost ◽  
Computation Time ◽  
Clinical Environment ◽  
Healthcare Organizations ◽  
Rigid Registration ◽  
Demons Algorithm ◽  
Point To Point

Over recent years, non-rigid registration has become a major issue in medical imaging. It consists in recovering a dense point-to-point correspondence field between two images, and usually takes a long time. This is in contrast to the needs of a clinical environment, where usability and speed are major constraints, leading to the necessity of reducing the computation time from slightly less than an hour to just a few minutes. As financial pressure makes it hard for healthcare organizations to invest in expensive high-performance computing (HPC) solutions, cluster computing proves to be a convenient solution to our computation needs, offering a large processing power at a low cost. Among the fast and efficient non-rigid registration methods, we chose the demons algorithm for its simplicity and good performances. The parallel implementation decomposes the correspondence field into spatial blocks, each block being assigned to a node of the cluster. We obtained an acceleration of 11 by using 15 2GHz PC's connected through a 1GB/s Ethernet network and reduced the computation time from 40min to 3min30. In order to further optimize the costs and the maintenance load, we investigate in the second part the transparent use of shared computing resources, either through a graphic client or a Web one.

scholarly journals High-availability on-site deployment to heterogeneous architectures for Project 8 and ADMX

2020 ◽  
Vol 245 ◽  
pp. 07029
Author(s):  
Benjamin LaRoque
Keyword(s):  
Coupling Parameter ◽  
Low Cost ◽  
Rapid Development ◽  
High Availability ◽  
Raspberry Pi ◽  
Development Environment ◽  
Heterogeneous Architectures ◽  
Active Research ◽  
Support Software ◽  
And Control

Project 8 is applying a novel spectroscopy technique to make a precision measurement of the tritium beta-decay spectrum, resulting in either a measurement of or further constraint on the effective mass of the electron antineutrino. ADMX is operating an axion haloscope to scan the mass-coupling parameter space in search of dark matter axions. Both collaborations are executing medium-scale experiments, where stable operations last for three to nine months and the same system is used for development and testing between periods of operation. It is also increasingly common to use low-cost computing elements, such as the Raspberry Pi, to integrate computing and control with custom instrumentation and hardware. This leads to situations where it is necessary to support software deployment to heterogeneous architectures on rapid development cycles while maintaining high availability. Here we present the use of docker containers to standardize packaging and execution of control software for both experiments and the use of kubernetes for management and monitoring of container deployment in an active research and development environment. We also discuss the advantages over more traditional approaches employed by experiments at this scale, such as detached user execution or custom control shell scripts.

scholarly journals Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Performance ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Raspberry Pi ◽  
Embedded Devices ◽  
Iot Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

scholarly journals Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3127
Author(s):  
Giuseppe Loprencipe ◽  
Flavio Guilherme Vaz de Almeida Filho ◽  
Rafael Henrique de Oliveira ◽  
Salvatore Bruno
Keyword(s):  
High Performance ◽  
Ride Comfort ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Road Networks ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
Urban Road ◽  
Comfort Index

Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system (GPS) module, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to indirectly monitor the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was used. Considering 21 km of roads with different levels of pavement decay, validation measurements were performed using the novel sensor, a high performance inertial based navigation sensor, and a road surface profiler. Therefore, comparisons between awz determined with accelerations measured on the two different inertial sensors are made; in addition, also correlations between awz, and typical pavement indicators such as international roughness index, and ride number were also performed. The results showed very good correlations between the awz values calculated with the two inertial devices (R2 = 0.98). In addition, the correlations between awz values and the typical pavement indices showed promising results (R2 = 0.83–0.90). The proposed sensor may be assumed as a reliable and easy-to-install method to assess the pavement conditions in urban road networks, since the use of traditional systems is difficult and/or expensive.

scholarly journals Development of Digital Signage for Primary School using Raspberry Pi

2021 ◽  
Vol 12 (3) ◽  
pp. 1394-1399
Author(s):  
Teddy Surya Gunawan Et.al
Keyword(s):  
Primary School ◽  
Low Cost ◽  
Web Server ◽  
Raspberry Pi ◽  
Digital Signage ◽  
Single Board Computer ◽  
Daily Schedule ◽  
School Purpose ◽  
Teachers And Parents ◽  
Informative Content

Nowadays, digital signage is a modern advertisement alternative that took over billboards and other traditional advertisements. In the context of education, digital signage provides students, teachers, and parents with an effective way of communication. This research proposed designing and developing low-cost digital signage using Raspberry Pi to be stationed at primary school. An LCD monitor and Raspberry Pi 4 single-board computer were utilized to display various informative content, such as examination date, daily schedule, and other announcements. Two methods were proposed, including Screenly and WordPress on a web server. The WordPress version needs to install PHP, MySQL, and Nginx web server, which can be accessed and updated remotely. Results showed that the free version of Screenly would be adequate for a simple announcement, but the developed WordPress version will be more appropriate and flexible for the primary school purpose.

scholarly journals MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms

2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Ruiqi Chen ◽  
Tianyu Wu ◽  
Yuchen Zheng ◽  
Ming Ling
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Low Cost ◽  
Raspberry Pi ◽  
Future Research ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Field Programmable ◽  
Ip Cores ◽  
Cost Efficient

In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML) algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, and high-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IP cores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achieve comprehensive performance in various tasks. With Verilog, we deploy and accelerate Artificial Neural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support Vector Machines (SVMs) on 10 different FPGA development boards from seven producers. Additionally, we analyze and evaluate our design with six datasets, and compare the best-performing FPGAs with traditional SoC-based systems including NVIDIA Jetson Nano, Raspberry Pi 3B+, and STM32L476 Nucle. The results show that Lattice’s ICE40UP5 achieves the best overall performance with low power consumption, on which MLoF averagely reduces power by 891% and increases performance by 9 times. Moreover, its cost, power, Latency Production (CPLP) outperforms SoC-based systems by 25 times, which demonstrates the significance of MLoF in endpoint deployment of ML algorithms. Furthermore, we make all of the code open-source in order to promote future research.

scholarly journals An Embedded System for applying High Performance Computing in Educational Learning Activity

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene Erlyn Wina Rachmawan ◽  
Nurul Fahmi ◽  
Edi Wahyu Widodo ◽  
Samsul Huda ◽  
M. Unggul Pamenang ◽  
...  
Keyword(s):  
Embedded System ◽  
High Performance Computing ◽  
High Performance ◽  
Cluster Computing ◽  
Low Cost ◽  
Learning Activity ◽  
Learning Satisfaction ◽  
Parallel Case ◽  
High Performance Computing Cluster ◽  
Performance Computing

HPC (High Performance Computing) has become more popular in the last few years. With the benefits on high computational power, HPC has impact on industry, scientific research and educational activities. Implementing HPC as a curriculum in universities could be consuming a lot of resources because well-known HPC system are using Personal Computer or Server. By using PC as the practical moduls it is need great resources and spaces.  This paper presents an innovative high performance computing cluster system to support education learning activities in HPC course with small size, low cost, and yet powerful enough. In recent years, High Performance computing usually implanted in cluster computing and require high specification computer and expensive cost. It is not efficient applying High Performance Computing in Educational research activiry such as learning in Class. Therefore, our proposed system is created with inexpensive component by using Embedded System to make High Performance Computing applicable for leaning in the class. Students involved in the construction of embedded system, built clusters from basic embedded and network components, do benchmark performance, and implement simple parallel case using the cluster.  In this research we performed evaluation of embedded systems comparing with i5 PC, the results of our embedded system performance of NAS benchmark are similar with i5 PCs. We also conducted surveys about student learning satisfaction that with embedded system students are able to learn about HPC from building the system until making an application that use HPC system.

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