New monitoring interface for the AMS experiment Hashmani, Raheem Karim; Konyushikhin, Maxim; Shan, Baosong ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
01/2023, Letnik:
1046
Journal Article
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The Alpha Magnetic Spectrometer (AMS) is constantly exposed to harsh condition on the ISS. As such, there is a need to constantly monitor and perform adjustments to ensure the AMS operates safely and ...efficiently. With the addition of the Upgraded Tracker Thermal Pump System, the legacy monitoring interface was no longer suitable for use. This paper describes the new AMS Monitoring Interface (AMI). The AMI is built with state-of-the-art time series database and analytics software. It uses a custom feeder program to process AMS Raw Data as time series data points, feeds them into InfluxDB databases, and uses Grafana as a visualization tool. It follows modern design principles, allowing client CPUs to handle the processing work, distributed creation of AMI dashboards, and up-to-date security protocols. In addition, it offers a more simple way of modifying the AMI and allows the use of APIs to automate backup and synchronization. The new AMI has been in use since January 2020 and was a crucial component in remote shift taking during the COVID-19 pandemic.
•Alpha Magnetic Spectrometer monitoring data.•Time series database.•Time series data analytics.•New Monitoring Interface.•Grafana.
Air traffic management (ATM) requires the handling big data of moving objects, such as flight trajectories. There is, however, a lack of specialized tools for trajectory-data management, where the ...spatiotemporal data are first class citizens. Instead, specialized algorithms for trajectory-data management are built on top of existing geospatial tools. In this paper, we showcase MobilityDB, which is an open-source database for moving objects. MobilityDB is developed as an extension to PostgreSQL and PostGIS that specializes in the storage and processing of trajectory data. Its data model integrates spatiotemporal and temporal types as first class citizens in the database. It thus allows one to perform complex spatial and spatiotemporal queries. This paper presents how to combine MobilityDB with Grafana, an open-source dashboard tool, to perform basic and advanced queries and interact with Grafana visualization. A use case for flight trajectories, based on the OpenSky Network data, is illustrated.
Energy storage through Lithium-ion Batteries (LiBs) is acquiring growing presence both in commercially available equipment and research activities. Smart power grids, e.g. smart grids and microgrids, ...also take advantage of LiBs to deal with the intermittency of renewable energy sources and to provide stable voltage. In this context, monitoring and data acquisition tasks are required for the proper operation and continuous surveillance and tracking of the LiB. In this paper, a monitoring system devoted to visualizing the operation of a LiB is presented. Internet of Things (IoT) technology is used to deploy the system, namely, Grafana software is applied for data analytics and visualization, being hosted in a microcomputer Raspberry Pi. The user is able to access online to graphical and numerical real time information about the LiB magnitudes (current, voltage, temperature, state of charge, etc.). Such a LiB acts as the backbone of a microgrid which hybridizes photovoltaic power with hydrogen generation and consumption. The proposal is a novelty in scientific literature since it overcomes limitations identified in previous works such as absence of long-term operation, of medium-scale power/capacity, of alerts for safe range of critical magnitudes, of real operating conditions, and of compatibility/interoperability management. The design and implementation of the monitoring system is reported together with experimental data of the LiB to prove its feasibility and successful performance.
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•Lithium-ion Batteries (LiBs) are gaining market presence and R&D efforts.•Internet of Things (IoT) is applied to deploy real time monitoring system for a LiB.•The LiB acts as backbone of microgrid with photovoltaic energy and hydrogen.•Novelty relies on IoT, mid-scale LiB, alerts, real conditions and interoperability.•Long-term (two years) experimental results prove the suitability of the proposal.
The increasingly massive use of digital technology requires that the application architecture be designed to have high availability and reliability. This is because when an application cannot be ...accessed, it will cause no small loss to the organization. Therefore, the development and operation teams must be able to detect when their system is not working well. For that, we need a system that can monitor application performance. In this research, a system is developed to collect telemetry data, namely metrics and traces from an online donation backend application based on the REST API. OpenTelemetry produces telemetry as an open-source telemetry instrumentation tool. Then the telemetry data is collected by the OpenTelemetry Collector which is then stored on the backend of each telemetry. Metrics are sent to Prometheus and traces are sent to Jaeger. The data metrics collected are throughput, request latency, and error rate which are visualized using the Grafana dashboard. The test results show that the monitoring system can collect real-time metrics data with an average delay of 13,8 seconds. The system can also detect when an anomaly occurs in the app and sends notifications via Slack. In addition, the trace data collected can be used to simplify the debugging process when an error occurs in the application. However, the implementation of OpenTelemetry in a REST API-based backend application to monitor metrics and traces has a negative impact on the performance of the application itself, which can reduce the number of request throughput with an average decrease of 23.32% and increase request latency with an average increase of 22.80%.
Seiring perkembangan teknologi yang begitu pesat, telah muncul banyak metode untuk manajemen dan analisis log dari sebuah komputer diantaranya metode Grafana Loki dan ELK Stack. Sehingga dampak dari ...perkembangan ini menimbulkan banyak variasi dan ketidaktahuan para administrator dalam menentukan metode mana yang sesuai dengan kebutuhan mereka. Pada penelitian ini menganalisis performa dari kedua metode tersebut terhadap server honeypot saat terjadi serangan dengan parameter penggunaan CPU dan Memori, kedua parameter tersebut merupakan standar untuk para administrator dalam mempertimbangkan metode yang akan dipilih. Kesimpulan dari penelitian ini bahwa berdasarkan parameter yang digunakan metode Grafana Loki lebih efisien dari segi pemakaian CPU dan Memori dibandingkan metode ELK Stack, Grafana Loki sangat ringan untuk diimplementasikan tetapi dengan fitur yang terbatas, sedangkan ELK Stack lebih banyak memakai resource CPU dan Memori tetapi mempunyai fitur yang lebih lengkap.Kata Kunci : Performa, Honeypot, ELK Stack, Grafana Loki
In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited ...resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.
Due to the memory limitation challenges of using low-cost computer interfacing hardware in long-term monitoring of signals, we present a flexible and easy-to-deploy wi-fi server-client platform. It ...allows using low-cost Raspberry Pi computers to interface hardware in the laboratory and use up-to-date software tools such as time-series database software. This facilitates long-term monitoring and data management in a local area network (LAN). RedStat consists of an installation script that helps configuring the LAN hostname and installs the open software tools required to interface and manage the data monitoring. Node-RED acts as the orchestrator of the software integration and RedStat supplies three different example programs to interface three open hardware projects: the microcontroller Arduino, the hardware incubator “OpenTCC” and the open-source potentiostat “DStat”. RedStat provides an infrastructure to develop similar programs interacting other types of serial communication devices, thus facilitating developing and increasing the impact of open-source hardware in continuous monitoring and condition-controlled experiments.
Design and implementation of an open-source-based supervisory control and data acquisition (SCADA) system for a community solar-powered reverse osmosis are presented in this paper. A typical SCADA ...system available on the market is proprietary and has a high initial and maintenance cost. Aside from that, there is no SCADA system with an alert system available to give users updates and status information concerning the system. The objective of this study is to develop a comprehensive SCADA design that takes advantage of open-source technology to address the world's most pressing problem, access to clean water. The designed reverse Osmosis system also uses renewable energy-based power sources. In this system, all data is stored and analyzed locally, which ensures the data is secure and allows the user to make data-driven decisions based on the collected data. Among the main components of this system are the field instrument devices (FIDs), the remote terminal unit (RTU), the main terminal units (MTUs), the web-based programming software, and the data analytics software. The Node-Red programming and dashboard tool, Grafana for data analytics, and InfluxDB for database management run on the main terminal unit having Debian operating system. Data is transmitted from the FIDs to the RTU, which then redirects it to the MTU via serial communication. Node-Red displays the data processed by the MTU on its dashboard as well, as the data is stored locally on the MTU and is displayed by means of Grafana, which is also installed on the same MTU. Through the Node-Red dashboard, the system is controlled, and notifications are sent to the community.
This paper provides a complete guide to the development, testing, and monitoring of a low-cost big data cluster through a detailed step-by-step configuration and installation of Apache Hadoop using 9 ...Raspberry Pis 4B. For the tests and performance evaluation, were used the Terasort and TestDFSIO benchmarks. The benchmarks were performed in different sizes of data files (250 MB up to 1 GB) and different slaves nodes quantity (2, 4, and 8). The results showed that the combination of Raspberry Pi and Apache Hadoop can be a very efficient and robust solution to get a low-cost big data cluster, considering its costs/benefits. Using a Raspberry Pi 3B+ as a monitoring server, we installed the Zabbix and Grafana tools, making it possible to collect important information in real-time, helping to better monitoring of the cluster’s devices and better visualization of the behavior and performance of the cluster.
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•Development of a low-cost big data cluster using Apache Hadoop and Raspberry Pi 4B.•Detailed step-by-step to guide the cluster development.•Cluster evaluation using the Terasort and TestDFSIO Benchmarks.•Cluster monitoring using a Raspberry Pi 3B+ as monitoring server with Zabbix and Grafana tools.
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