Cloud Monitoring Research Articles

Design of Water Quality Monitoring Visualization System Based on Cloud Platform

In response to the problem of domestic and severe water pollution control, in order to help the water environment monitoring department, this paper proposes a structure of B/S/D as architecture, Spring Boot + MyBatis + MySQL + CXF for the programming framework Water quality visual cloud monitoring system. The system abandoned the traditional method using Spring/Spring MVC as a program framework, using the Spring Boot new frame structure, which greatly enhances the readability of programming simple and program, and changed the lengthy, low running rate, and front end of the web page. Slow disadvantages. The system is actually developed, and the deployment results show that the system operates stable, and can perform long-term continuous, real-time monitoring, and can send real-time data and historical data to the information terminal to provide reliable cases and solutions to strengthen water quality supervision.

Elastic Performance Test Method of Web Server in Cloud Computing Environment

In the process of traditional web server elastic performance test, the performance of tracking data is poor, which leads to excessive server jitter and poor accuracy of elastic test results. Therefore, this paper studies the network server elastic performance test method in cloud computing environment. In this method, cloud monitoring technology is used to track the operation data of Web server. According to the multi platform call mode of Web server, a statistical regression model is established to determine the elasticity measurement index. The load balancing algorithm is used to test the server load balancing to obtain the elasticity value of Web server. Experimental results: in the whole running period of Web server, the jitter times of the proposed method are 15.6066 times, 16.5600 times and 16.5733 times lower than those of the three traditional methods, respectively. It can be seen that the new test method can accurately track the response ability of the server and obtain more accurate elasticity value.

Earth Imaging From the Surface of the Moon With a DSCOVR/EPIC-Type Camera

The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) satellite observes the entire Sun-illuminated Earth from sunrise to sunset from the L1 Sun-Earth Lagrange point. The L1 location, however, confines the observed phase angles to ∼2°–12°, a nearly backscattering direction, precluding any information on the bidirectional surface reflectance factor (BRF) or cloud/aerosol phase function. Deploying an analog of EPIC on the Moon’s surface would offer a unique opportunity to image the full range of Earth phases, including observing ocean/cloud glint reflection for different phase angles; monitoring of transient volcanic clouds; detection of circum-polar mesospheric and stratospheric clouds; estimating the surface BRF and full phase-angle integrated albedo; and monitoring of vegetation characteristics for different phase angles.

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications.

Active disturbance rejection-based industrial cascade control plant with cloud monitoring

PurposeThe purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with a robust control strategy.Design/methodology/approachIn this paper, an active disturbance rejection control (ADRC) strategy is used for the two tank level process plant and it is remotely monitored with the industrial internet of things technology. The disturbances in a primary and secondary loop of the cascade process, which are affecting the overall settling time (ts) of the process, are eliminated by using the proposed, ADRC-ADRC structure in the cascade loop. The stability of the proposed controller is presented with Hurwitz’s stability criteria for selecting gains of observers. The results of the proposed controller are compared with the existing active disturbance rejection control-proportional (ADRC-P) and proportional-integral derivative-proportional (PID-P)-based controller by experimental validation.FindingsIt is observed that the settling time (ts) in the case of the proposed controller is improved by 60% and 55% in comparison to PID-P and ADRC-P, respectively. The level process is interfaced with an industrial controller and real-time data acquired in matrix laboratory (MATLAB), which acted as a remote monitoring platform for the cascade process.Originality/valueThe proposed controller is designed to provide robustness against disturbance and parameter uncertainty. This paper provides an alternate way for researchers who are using MATLAB and ThingSpeak cloud server as a tool for the implementation.

Exploration on Water Pollution Cloud Monitoring Method Based on Spectral Method

Exploration on Water Pollution Cloud Monitoring Method Based on Spectral Method

Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking.

Intelligent wireless networks that comprise self-organizing autonomous vehicles equipped with punctual sensors and radio modules support many hostile and harsh environment monitoring systems. This work’s contribution shows the benefits of applying such networks to estimate clouds’ boundaries created by hazardous toxic substances heavier than air when accidentally released into the atmosphere. The paper addresses issues concerning sensing networks’ design, focussing on a computing scheme for online motion trajectory calculation and data exchange. A three-stage approach that incorporates three algorithms for sensing devices’ displacement calculation in a collaborative network according to the current task, namely exploration and gas cloud detection, boundary detection and estimation, and tracking the evolving cloud, is presented. A network connectivity-maintaining virtual force mobility model is used to calculate subsequent sensor positions, and multi-hop communication is used for data exchange. The main focus is on the efficient tracking of the cloud boundary. The proposed sensing scheme is sensitive to crucial mobility model parameters. The paper presents five procedures for calculating the optimal values of these parameters. In contrast to widely used techniques, the presented approach to gas cloud monitoring does not calculate sensors’ displacements based on exact values of gas concentration and concentration gradients. The sensor readings are reduced to two values: the gas concentration below or greater than the safe value. The utility and efficiency of the presented method were justified through extensive simulations, giving encouraging results. The test cases were carried out on several scenarios with regular and irregular shapes of clouds generated using a widely used box model that describes the heavy gas dispersion in the atmospheric air. The simulation results demonstrate that using only a rough measurement indicating that the threshold concentration value was exceeded can detect and efficiently track a gas cloud boundary. This makes the sensing system less sensitive to the quality of the gas concentration measurement. Thus, it can be easily used to detect real phenomena. Significant results are recommendations on selecting procedures for computing mobility model parameters while tracking clouds with different shapes and determining optimal values of these parameters in convex and nonconvex cloud boundaries.

Hybrid energy design for lighter than air systems

Solar Powered Lighter Than Air (LTA) systems find applications in reconnaissance, surveillance, mapping of geographical locations, and environmental monitoring using electronic and communication sensor payloads. The system payload comprises an airborne LiDAR for cloud monitoring and a day-night vision camera for surveillance applications. The primary source of payload power is generated by lightweight and flexible solar photovoltaic modules integrated on the platform’s top hull. The generated solar power is affected by wind velocity disturbances, dynamic cloud footprints, and inherent platform contour. The current objective is to design a hybrid energy scheme for uninterrupted payload operation in the presence of the aforementioned degrading factors. A zero-energy deficit design consisting of a diesel generator as an auxiliary power source has been proposed. The novelty of work lies in considering the combined effect of impact factors in the energy scheme for LTA system applications. Under the given environmental conditions, a power outage of 2.5 hours out of 11 operating hours was observed, which is satisfactorily compensated using the proposed hybrid design. The work presents detailed design of a practical and scalable hybrid system which ensures uninterrupted power under realistic environmental conditions.

A Positioning System on Robot for Agricultural Environment

Focus on the complex environment in the wild, we designed a positioning system for agricultural environment. This system has multiple parts such as SLAM robot, remote console, mobile monitoring terminal, cloud monitoring platform, which fully fulfills the requirements of the dynamic positioning system. The system takes into account the characteristics of wide positioning range and large coverage area of the SLAM robot, which can greatly reduce the positioning complexity of the system and reduce the required positioning equipment, while reducing operating costs and increasing the coverage area.

Epidemiological Characteristics of the Corona Virus Disease 2019 in Shaanxi Province Based on “Internet +” Epidemic Monitoring Cloud Platform

Since the outbreak of the Corona Virus Disease 2019(COVID-19), medical staffs have reported cases of hospital infection of COVID-19, which has greatly affected the physical and mental health of medical staff. In order to effectively avoid the immunization situation and prevent the front-line medical staff from collecting information and reporting on the new coronary pneumonia patients, suspected patients, close contacts and other personnel, the possibility of being infected due to various direct and indirect contacts may arise. Information technology, the establishment of a new cloud monitoring platform based on “Internet +” for coronary epidemic outbreaks, and the realization of “zero touch” operation and “paperless” management of the entire process of information collection, confirmation, review and reporting, which greatly reducing the front-line work. The infection risk and labor intensity of personnel have reduced the workload and improved the efficiency of information collection. At the same time, it has also realized automatic statistics, intelligent statistical description and synchronous sharing of the information.

Near-real-time volcanic cloud monitoring: insights into global explosive volcanic eruptive activity through analysis of Volcanic Ash Advisories

Understanding the location, intensity, and likely duration of volcanic hazards is key to reducing risk from volcanic eruptions. Here, we use a novel near-real-time dataset comprising Volcanic Ash Advisories (VAAs) issued over 10 years to investigate global rates and durations of explosive volcanic activity. The VAAs were collected from the nine Volcanic Ash Advisory Centres (VAACs) worldwide. Information extracted allowed analysis of the frequency and type of explosive behaviour, including analysis of key eruption source parameters (ESPs) such as volcanic cloud height and duration. The results reflect changes in the VAA reporting process, data sources, and volcanic activity through time. The data show an increase in the number of VAAs issued since 2015 that cannot be directly correlated to an increase in volcanic activity. Instead, many represent increased observations, including improved capability to detect low- to mid-level volcanic clouds (FL101–FL200, 3–6 km asl), by higher temporal, spatial, and spectral resolution satellite sensors. Comparison of ESP data extracted from the VAAs with the Mastin et al. (J Volcanol Geotherm Res 186:10–21, 2009a) database shows that traditional assumptions used in the classification of volcanoes could be much simplified for operational use. The analysis highlights the VAA data as an exceptional resource documenting global volcanic activity on timescales that complement more widely used eruption datasets.

Key Technologies and Applications of Wild Animal Satellite Tracking

Mastering the laws of wildlife activities and family territory is an important task for wildlife protection, habitat restoration, and epidemic surveillance. Aiming at the shortcomings of traditional wildlife monitoring methods, an overall framework of wild animal satellite tracking system is proposed, and key technologies such as MEMS design, energy optimization, miniaturization and lightweight, wearable methods, and big data platform development are discussed and practiced. The results show that the wild animal satellite tracker developed by the above technology has the characteristics of miniaturization and lightweight, which can better solve the problem of energy supply in the field, and the service life can reach more than 5 years. Cloud recording of wild animal tracking data, Cloud Storage, Cloud computing, and Cloud monitoring. It plays a role in ensuring the data security of China’s wildlife ecological resources and other sensitive geographic information, and also provides scientific and effective technology tools and data support for China’s wildlife research, protection, and management, and is of great significance for serving the national ecological civilization construction.

Visualization Technology Framework of Industrial Cloud Computing

Industrial cloud computing is considered to be an essential technology for future industrial enterprises. Under the influence of Industry 4.0 and Industrial Internet concepts, industrial cloud computing related technologies are becoming a hot topic in recent research. How to observe and analyze the intermediate and final results of industrial cloud computing is the first problem to be solved in this research field. This paper proposes a new industrial PaaS (Platform-as-a-Service) architecture, which has a very important relationship with the visualization of industrial cloud computing, and it deploys a large number of structurally symmetric Data Engine computing units. Configuration and reusable methods are used in this paper to form a visualization technical framework and establish a data mapping relationship with the Data Engine, which significantly improves the efficiency of visualization application development. Finally, we validated the technology using an industrial internet test bed. The experimental results show that this visualization framework technology can provide industrial users with an advanced industrial application development environment, and effective realtime monitoring of industrial cloud computing through the internet.

Frequency Management Method Based on Cloud Computing, Big Data and Artificial Intelligence

Today’s electromagnetic environment is still not optimistic enough, and spectrum resources are relatively few. In the actual allocation, unevenness will inevitably occur. In addition, the current level of intelligent monitoring is limited, and it is impossible to fully grasp the dynamics of frequency use. Spectrum monitoring emergency maneuverability still needs to be further improved, and there is a lack of refined spectrum resource management measures. In order to solve this problem as soon as possible, the article proposed a solution to the electromagnetic spectrum monitoring problem based on Cloud Computing, Big Data and Artificial Intelligence technology, proposed construction plans for various main applications and corresponding monitoring services, mainly strengthening the construction of handheld monitoring systems, electromagnetic spectrum monitoring and control systems, cloud monitoring systems, Big Data analysis systems, and intelligent monitoring and dispatching systems.

Anomaly detection in the CERN cloud infrastructure

Anomaly detection in the CERN OpenStack cloud is a challenging task due to the large scale of the computing infrastructure and, consequently, the large volume of monitoring data to analyse. The current solution to spot anomalous servers in the cloud infrastructure relies on a threshold-based alarming system carefully set by the system managers on the performance metrics of each infrastructure’s component. This contribution explores fully automated, unsupervised machine learning solutions in the anomaly detection field for time series metrics, by adapting both traditional and deep learning approaches. The paper describes a novel end-to-end data analytics pipeline implemented to digest the large amount of monitoring data and to expose anomalies to the system managers. The pipeline relies solely on open-source tools and frameworks, such asSpark, Apache Airflow, Kubernetes, Grafana, Elasticsearch. In addition, an approach to build annotated datasets from the CERN cloud monitoring data is reported. Finally, a preliminary performance of a number of anomaly detection algorithms is evaluated by using the aforementioned annotated datasets.

Monitoring Next Cloud sebagai Private Cloud Storage dengan Notifikasi Telegram

Penyimpanan online seperti Cloud Computing merupakan platform yang paling banyak digunakan sebagai media penyimpanan online yang dapat diakses dimanapun dan kapanpun. Saat ini Cloud Computing mengalami perkembangan pesat. Salah satu diantaranya adalah menyediakan media penyimpanan yang dapat digunakan user untuk menyimpan data dengan aman. Salah satu Open Source yang menyediakan layanan media penyimpanan adalah Nextcloud. Nextcloud merupakan penyedia layanan penyimpanan gratis dan Open Source yang memiliki fitur keamanan dan kemampuan monitoring cloud. Informasi monitoring cloud disampaikan oleh Bot Telegram. User Telegram dapat berkomunukasi dengan bot telegram untuk mendapatkan informasi monitoring tentang Nextcloud. Terdapat beberapa perintah yang digunakan untuk mendapatkan informasi Monitoring

A multi-sensor satellite-based archive of the largest SO<sub>2</sub> volcanic eruptions since 2006

Abstract. We present a multi-sensor archive collecting spatial and temporal information about volcanic SO2 clouds generated by the 11 largest eruptions of this century. The detection and monitoring of volcanic clouds are an important topic for aviation management, climate issues and weather forecasts. Several studies focusing on single eruptive events exist, but no archive available at the moment combines quantitative data from as many instruments. We archived and collocated the SO2 vertical column density estimations from three different satellite instruments (AIRS, IASI and GOME-2), atmospheric parameters as vertical profiles from the Global Navigation Satellite Systems (GNSS) Radio Occultations (RO), and the cloud-top height and aerosol type from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Additionally, we provide information about the cloud-top height from three different algorithms and the atmospheric anomaly due to the presence of the cloud. The dataset is gathering 206 d of SO2 data, collocated with 44 180 backscatter profiles and 64 764 radio occultation profiles. The modular structure of the archive allows an easy collocation of the datasets according to the users' needs, and the cross-comparison of the datasets shows different consistency of the parameters estimated with different sensors and algorithms, according to the sensitivity and resolution of the instruments. The data described here are published with a DOI at https://doi.org/10.5880/fidgeo.2020.016 (Tournigand et al., 2020a).

The landscape from above: Continuous cloud monitoring for continuous assurance

The concept of monitoring information system security has long been recognised as sound and valuable management practice. For additional consideration, a large portion of compliance requirements for information security and privacy are supported by such monitoring. Security programmes must be aligned with privacy and compliance programmes to ensure those areas of data protection compliance are appropriately met and monitored, and then actions based on maturity levels must be aligned with information assurance programmes. Some key areas to consider in information security programmes include: 1) Continuous assurance (full data life cycle, continuous monitoring, continuous awareness, continuous compliance, challenges, benefits); 2) continuous supply chain management (continuous vendor management and oversight, benefits, challenges); 3) continuous cloud assurance (private cloud, community cloud, public cloud, hybrid cloud); and 4) continuous improvement (what is involved and necessary, including actions, monitoring and metrics). This paper posits that organisations, building out their digital transformation strategies, must think strategically about the way in which they manage privacy compliance in the cloud, committing to a data-driven continuous assurance privacy programme which would provide a more robust compliance posture.

Cloud-Manufacturing-Based Condition Monitoring Platform With 5G and Standard Information Model

Condition monitoring (CM) is the escort of smart manufacturing, which guarantees safety, precision, and efficiency of production process. However, traditional CM capacities with limited storage and computing resources cannot follow the explosive growth of manufacturing data. Empowered by the emerging Internet of Things, cyber–physical system, and cloud computing, cloud manufacturing (CMfg) characterizes a new service-oriented technology, which is reshaping the CM paradigm into an agile, scalable, and interoperable mode. In this article, a CMfg-based CM platform for smart manufacturing is proposed. A 5G wireless communication network-based edge computing system is presented to achieve data acquisition, feature extraction, and emergency response with ultrareliability and low-latency communication. In order to deal with the issue of data management and analysis, a generic open platform communications unified architecture information model oriented to the workshop is proposed in the fog layer. The cloud monitoring platform is constructed encompassing message delivery, data storage, status visualization, and training of artificial intelligence models. Finally, a CMfg-based CM system and a CMfg-based tool wear estimation system are outlined as two industrial cases. The experiments verify the feasibility and effectiveness of the proposed platform.

A Multi-Agent-Based Data Collection and Aggregation Model for Fog-Enabled Cloud Monitoring

The fog-enabled cloud computing has received considerable attention as the fog nodes are deployed at the network edge to provide low latency. It involves various activities, such as configuration management, security management, and data management. Monitoring these activities is essential to improve performance and QoS of fog computing infrastructure. Data collection and aggregation are the basic tasks in the monitoring process, and these phases consume more communicational power as the IoT nodes generate a huge amount of redundant data frequently. In this paper, a multi-agent-based data collection and aggregation model is proposed for monitoring fog infrastructure. The data collection model adopts a hybrid push-pull algorithm that updates the data when a certain change in new data compared to old data. A tree-based data aggregation model is developed to reduce communication overhead between fog node and cloud. The experimental results show that the proposed model improves data coherency and reduces communication overhead compared to existing data collection and aggregation models.