Data Processing Center Research Articles

Role of green data center in human resources development model

Human resources are now a very important factor in the development of various enterprises and even countries, and how to master and manage human resources has always been a problem. Nowadays, the development of science and technology has made the processing of various data simple, and green data processing centers that save energy have also been established. This paper is to study the role of green data center in terms of human resources development model, by using mixed integer linear programming optimization mathematical model of the existing data center, reduce data center energy consumption NRE while establishing a new green data center, and the data center conducts related research on the information search of human resources and its auxiliary role on how to efficiently hire and train employees. The research results show that the green data center can play a great role in the development of human resources. Compared with relying solely on human resource managers, the use of green data centers to manage human resources has increased the decision-making ability of corporate personnel by 12 %, and personnel efficiency has increased by about 10 %, basically realizing comprehensive and effective management of human resources.

Разработка математической модели управления железнодорожными грузовыми перевозками в мультимодальном сообщении

Objective: To develop a mathematical model of the functioning of the freight traffic subsystem interconnected with the general freight transportation model on the basis of the accepted basic assumptions and conditions, since the main type of interaction of the general system with a particular system (subsystem) is the application of constraints on the particular model parameters imposed by the requirements for the quality of the general model functioning according to the nomenclature, service level, and time characteristics of customer service, which are external to the modeled transportation subsystem. Methods: Analysis, synthesis, abstraction, comparison, and mathematical modeling methods were used. Results: A mathematical model of the functioning of a transportation subsystem has been created, taking into account the necessary restrictions imposed by the Russian Railways general functioning model. The complete package of source data required for the practical implementation of this mathematical model as applied to railway transportation is available in the data stores of the main data processing center of Russian Railways. Existing numerical methods for solving such higher dimensional systems of equations make it possible to obtain a numerical implementation of such a model. The proposed model can serve the purposes of assessing the efficiency of the transportation subsystem, since it makes it possible to assess the mathematical expectations of the numbers of each of this system's states at any given time. Practical importance: The practical result of applying the developed model should be quantitatively substantiated recommendations for solving the main problems of choosing a transportation provider when planning multimodal transportation process, as well as for current and future transportation management under given conditions

Dimension Reduction Algorithm Based on Adaptive Maximum Linear Neighborhood Selection in Edge Computing

With the rapid development of the Internet of Things (IoT), large quantities of data have been generated. Due to the limitation of the network bandwidth, the time and energy consumption of data transmission are increased. Data feature information can be extracted in real-time by the deployment of a data processing center. In this article, a novel dimension reduction approach is proposed in edge computing. First, a four-layer data processing framework is designed for data acquisition. A task assignment algorithm (TAA) is used for the condition when the edge node stops working due to an accident. Second, a threshold strategy is proposed to filter the data and reduce the dimension. Finally, the dimension reduction algorithm based on adaptive maximum linear neighborhood selection (AMLNS) is proposed. The harmonic geodesic distance is introduced to avoid the deformation of the manifold structure in AMLNS algorithm. Particularly, multiple weights are used to construct linear structure, which has a better embedding effect than single weight. The maximum linear neighborhood error weight is used to calculate the data coordinates. Experimental results show that the TAA improves the task completion rate about 15% and 36% over the random assignment method in mobile layer and edge layer, respectively. Compared with the local linear embedding (LLE), the points distribution of AMLNS is more uniform and regular, the execution time of AMLNS is reduced by about 17%. Furthermore, the embedding errors are less than those of LLE.

Study on the Vibration Characteristics of the Telescope T80 in the Javalambre Astrophysical Observatory (JAO) Aimed at Detecting Invalid Images.

The location of large telescopes, generally far from the data processing centers, represents a logistical problem for the supervision of the capture of images. In this work, we carried out a preliminary study of the vibration signature of the T80 telescope at the Javalambre Astrophysical Observatory (JAO). The study analyzed the process of calculating the displacement that occurs because of the vibration in each of the frequencies in the range of interest. We analyzed the problems associated with very low frequencies by means of simulation, finding the most critical vibrations below 20 Hz, since they are the ones that generate greater displacements. The work also relates previous studies based on simulation with the real measurements of the vibration of the telescope taken remotely when it is subjected to different positioning movements (right ascension and/or declination) or when it performs movement actions such as those related to filter trays or mirror cover. The obtained results allow us to design a remote alarm system to detect invalid images (taken with excess vibration).

Samsara architecture: Exploring situation awareness in cloud computing management

Issues related to energy consumption and its efficiency in large-scale computing environments have emerged as critical points in the development of modern computer systems. This article presents the Samsara architecture, which aims to manage energy-efficient computational clouds. The architecture was developed exploring situation awareness strategies, operating autonomously and with minimal human intervention, essential aspects in massive data processing centers. Samsara has been implemented as one of the modules of the OpenStack cloud platform and considers the maximum allocation capacity of each physical machine, to consolidate the workloads. In the evaluations carried out with synthetic loads, reductions of up to 12.3% in the energy consumption in the managed computational infrastructure were achieved, demonstrating the potential of Samsara for the operation of computational clouds.

Local image processing in distributed monitoring system

In this paper an approach to local image processing in distributed monitoring systems is considered. The proposed approach makes possible to optimize traffic transmitted from monitoring devices generating images or video streams. Image processing, object recognition and identification are performed locally. Based on information received from all monitoring devices, including images, event logs are generated locally for their further transmission to the central data collection, processing and analysis unit. The method of local image processing was tested on the example of face recognition of people who took part in the experiment. Under various experimental conditions, it was found that the proposed method has a sufficiently high accuracy.

A novel communication framework between MANET and WSN in IoT based smart environment

Internet of Things (IoT) has become an emerging platform for connecting real-world objects for desired service provisioning via the Internet. The real-world devices or objects in IoT enable a smart environment with the help of Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). However, the integration of MANET and WSN in the IoT environment requires a flexible and unified framework for efficient and secure communication between intelligent objects or devices. The state-of-art works in this direction lack standard communication framework for effective implementation of MANET with WSN. In this paper, an efficient and secure communication framework for interconnecting MANET and WSN devices is presented. The proposed communication framework comprises different phases such as collecting data using sensors, sending the data to data processing centers via the mobile access points and finally forwarding the data to data centers for analysis, decision making and reporting purposes. The proposed integration solution is discussed in detail and is evaluated with a case study on data harvesting in rural areas. The efficacy of the proposed framework is reported which will significantly drive the integration of MANET and WSN in future internet using smart objects.

The Solar Orbiter Solar Wind Analyser (SWA) suite

The Solar Orbiter mission seeks to make connections between the physical processes occurring at the Sun or in the solar corona and the nature of the solar wind created by those processes which is subsequently observed at the spacecraft. The mission also targets physical processes occurring in the solar wind itself during its journey from its source to the spacecraft. To meet the specific mission science goals, Solar Orbiter will be equipped with both remote-sensing and in-situ instruments which will make unprecedented measurements of the solar atmosphere and the inner heliosphere. A crucial set of measurements will be provided by the Solar Wind Analyser (SWA) suite of instruments. This suite consists of an Electron Analyser System (SWA-EAS), a Proton and Alpha particle Sensor (SWA-PAS), and a Heavy Ion Sensor (SWA-HIS) which are jointly served by a central control and data processing unit (SWA-DPU). Together these sensors will measure and categorise the vast majority of thermal and suprathermal ions and electrons in the solar wind and determine the abundances and charge states of the heavy ion populations. The three sensors in the SWA suite are each based on the top hat electrostatic analyser concept, which has been deployed on numerous space plasma missions. The SWA-EAS uses two such heads, each of which have 360° azimuth acceptance angles and ±45° aperture deflection plates. Together these two sensors, which are mounted on the end of the boom, will cover a full sky field-of-view (FoV) (except for blockages by the spacecraft and its appendages) and measure the full 3D velocity distribution function (VDF) of solar wind electrons in the energy range of a few eV to ∼5 keV. The SWA-PAS instrument also uses an electrostatic analyser with a more confined FoV (−24° to +42° × ±22.5° around the expected solar wind arrival direction), which nevertheless is capable of measuring the full 3D VDF of the protons and alpha particles arriving at the instrument in the energy range from 200 eV/q to 20 keV/e. Finally, SWA-HIS measures the composition and 3D VDFs of heavy ions in the bulk solar wind as well as those of the major constituents in the suprathermal energy range and those of pick-up ions. The sensor resolves the full 3D VDFs of the prominent heavy ions at a resolution of 5 min in normal mode and 30 s in burst mode. Additionally, SWA-HIS measures 3D VDFs of alpha particles at a 4 s resolution in burst mode. Measurements are over a FoV of −33° to +66° × ±20° around the expected solar wind arrival direction and at energies up to 80 keV/e. The mass resolution (m/Δm) is > 5. This paper describes how the three SWA scientific sensors, as delivered to the spacecraft, meet or exceed the performance requirements originally set out to achieve the mission’s science goals. We describe the motivation and specific requirements for each of the three sensors within the SWA suite, their expected science results, their main characteristics, and their operation through the central SWA-DPU. We describe the combined data products that we expect to return from the suite and provide to the Solar Orbiter Archive for use in scientific analyses by members of the wider solar and heliospheric communities. These unique data products will help reveal the nature of the solar wind as a function of both heliocentric distance and solar latitude. Indeed, SWA-HIS measurements of solar wind composition will be the first such measurements made in the inner heliosphere. The SWA data are crucial to efforts to link the in situ measurements of the solar wind made at the spacecraft with remote observations of candidate source regions. This is a novel aspect of the mission which will lead to significant advances in our understanding of the mechanisms accelerating and heating the solar wind, driving eruptions and other transient phenomena on the Sun, and controlling the injection, acceleration, and transport of the energetic particles in the heliosphere.

Забезпечення доступності інформації у центрі обробки даних військового призначення на основі балансу продуктивності системи

Informatization of the Armed Forces of Ukraine involves the creation of information infrastructure and implementation of elements of the Unified Automated Management System of the Armed Forces of Ukraine. Data Processing Centers (DPCs) should become the software and hardware basis and the main component of the information infrastructure of the Ministry of Defense of Ukraine. In this regard, the performance indicators of the data center, such as its capacity and response time are important. Among the many factors affected by these indicators, the availability of information is more important. Accessibility of information means the state in which users who have access rights to information can implement them without hindrance. Access rights include: the right to read, modify, store, copy, destroy information. The current practice of assessing the performance of a Distributed Data Center Information System (DDCI) has only indicative character. The most rational approach to obtaining calculations of DDCI is the use of prognostic models. Knowing the DDCI performance forecast, you can predict the behavior of the system in different operating conditions and ensuring the availability of information in Data Center. The aim of the article is to develop an approach to ensure the availability of information to the data center based on the assessment of the balance of system performance. Modeling a Distributed Information System by the key performance indicators confirms the truth of the proposed approach and allows you to clearly assess the relationship between key performance indicators, the balance of which is the basis for ensuring the availability of information.

UTILIZATION OF BLUETOOTH CONNECTIONS FOR ARDUINO BASED DOOR LOCK SECURITY TOOLS

Home security is something that must be considered for every homeowner. Especially when you have to leave the house empty for work or even on vacation. You must be worried about thieves, lost jewelry, or the safety of other valuables. Therefore, the authors made a door lock security device by utilizing a Bluetooth connection with the Arduino Uno microcontroller. The research method used was direct observation of the selected object, namely the author's house, and conducting literature studies related to the Arduino Uno microcontroller. This door lock tool uses the Arduino Uno as a data processing center, because it is easy to use, and most importantly in the charging program, it does not require a download like a microcontroller in general. This Arduino Uno contains a special program that will give commands to the Servo Motor to open and open the door. Not only opening and opening doors, but this tool can also move the door without us holding or touching it but through a Smartphone. The purpose of this research is to produce a tool that can make it easier to manage or open doors using the Arduino via an Android smartphone using a Bluetooth connection.

Strategies to minimize preventable morbidity and mortality resulting from pandemics like COVID-19.

Strategies to minimize preventable morbidity and mortality resulting from pandemics like COVID-19.

Automatic Detection and Monitoring System of Pantograph–Catenary in China’s High-Speed Railways

With the rapid growth of China’s high-speed railway mileage, relevant safety technologies of railway operation are very urgent at present. One of the crucial parts is the pantograph–catenary system, which is directly related to the power supply safety of trains in high-speed railways. To ensure the safe operation of the pantograph–catenary system and eliminate the hidden danger during the power supply of trains, from 2011 in China, an automatic detection and monitoring system (called 6C system) of pantograph–catenary in high-speed railways was gradually established to timely discover and report all the potential defects and evaluate the performance of the pantograph–catenary system. The 6C system can measure the dynamic and static geometry parameters of the catenary and detect the defective working states of components and the operation conditions of the pantograph–catenary system. Considering the feedbacks from railway administrations, the 6C system can also realize a fine-grained detection and diagnosis of the pantograph–catenary and significantly improve the detection efficiency. In this article, the whole architecture of the 6C system is presented in detail, including six relatively independent parts for the detection and monitoring of pantograph–catenary systems and a data processing center with a three-level structure. Especially, some critical technologies for automatic detection and monitoring of pantograph–catenary systems are introduced. In the end, some new techniques of detection and monitoring are discussed, and suggestions are made for the future development of detection and monitoring systems of pantograph–catenary in high-speed railways.

Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network

The human brain network is one of the most complex data processing centers so far and shows several topographic features such as small worldness, modularity, and high efficiency. Inspired by the human brain network, the artificial neural network (ANN) method was proposed and has become an important engineering tool for pattern recognition. A recent study suggested the similarity between the human brain network and ANN. Would the ANN show topographic features that are similar to those of the human brain network? In the present study, we first constructed a simple backpropagation ANN (BP-ANN) with one hidden layer, which is one of the simplest, representative ANN, based on CIFAR10 and CIFAR100 datasets. Then, we explored the topographic features of a network with artificial neurons. We found that artificial neurons of the BP-ANN showed a high local efficiency and modularity similar to the human brain network. However, the global efficiency of artificial neurons of the BP-ANN was comparable with those of a random network. Our results showed that the best model (high accuracy and no overfitting) of a large-scale BP-ANN during epochs of training showed the lowest local and global efficiency. These results might imply that after epochs of training, neurons of the BP-ANN form local highly intra-connected functional modules but lack cooperation at the global level. The topography structure of the BP-ANN also shows the potential to become an early-stop marker during training.

Improving Methods and Facilities for the Evaluation of Earth Orientation Parameters at the MMC SSTF

The results of work carried out over the last five years to improve the methods and facilities of the Main Metrological Center of the State Service of Time, Frequency, and Determination of the Parameters of the Earth Rotation (MMC SSTF) are described. Modernization was carried out both in terms of hardware and software. As a result of the work carried out at the MMC SSTF, a software correlator has been established, computer facilities have been updated, and a Data Processing and Analysis Center has been created. The All-Russia Research Institute of Physicotechnical and Radio Measurements now possesses a new-generation satellite laser range finder created by the Scientific and Production Corporation Precision Instrumentation Systems. New software packages for processing laser measurements, including lunar laser range-finding measurements, were developed. Software providing metrological support for the spacecraft orbits of Russian and American global navigation satellite systems was developed. Software for processing measurements on radio interferometers with extra-long bases and joint data processing has been modernized. The development of facilities for determining and predicting the Earth’s rotation parameters took place in line with current international trends, allowing work aimed at determining and predicting parameters of the Earth’s rotation to be carried out at a high international level.

VIRTUALIZATION OF THE NETWORK INFRASTRUCTURE OF BELARUSIAN STATE UNIVERSITY

The article discusses current issues of virtualization of the network infrastructure of educational institutions. A new approach to creating a cloud virtualization environment for the network infrastructure of educational institution has been proposed. It is based on the joint use of virtualization technologies and software-defined networks. Virtual network infrastructure is a combination of technologies and architectural solutions. It provides users with mobility during an ongoing educational process, secures access to cloud information resources of the wireless virtual network of an educational institution and centralized Internet access control. A prototype of the virtual network infrastructure of the Belarusian State University has been built. The prototype implements a model that includes virtual network segments (subnets), with which services are provided to end users, and virtual segments that provide presentation of service functions. Virtualization technologies and software-defined networks allow to unify the process of creating network infrastructure, to scale the resources provided on demand with minimal organizational and technical costs, and to adapt the virtual infrastructure to the requirements of a particular educational institution. The proposed model using virtual network segments has been implemented in the data processing center of the Belarusian State University. To create a cloud environment, the solution based on OpenStack was chosen. The basis of the virtualization environment is cloud services, which provide the implementation of the functionality of a virtual network infrastructure.

Optimal Cooperative Relaying and Power Control for IoUT Networks With Reinforcement Learning

Internet of Underwater Things (IoUT) consists of numerous sensor nodes distributed in an underwater area for sensing, collecting, processing information, and sending related messages to the data processing center. However, the characteristics of the underwater environment will bring strict limitations on communication coverage and power scarcity to IoUT networks. Applying cooperative communications to IoUT networks can expand the communication range and alleviate power shortages. In this article, we investigate the cooperative communication problem in a power-limited cooperative IoUT system and propose a reinforcement learning-based underwater relay selection strategy. Specifically, we first determine the optimal transmit powers of the source node and the selected underwater relay to maximize the end-to-end signal-to-noise ratio of the system. Then, we formulate the underwater cooperative relaying process as a Markov process and apply reinforcement learning to obtain an effective underwater relay selection strategy. The simulation results show that the performance of the proposed scheme outperforms that of the equal transmit power settings under the same conditions. In addition, the proposed deep Q-network-based underwater relay selection strategy improves the communication efficiency compared with the Q-learning-based strategy, and the number of iterations needed for convergence can be effectively reduced.

On Connectivity of UAV-Assisted Data Acquisition for Underwater Internet of Things

Underwater exploration activities have grown significantly due to the proliferation of underwater Internet of Things (UIoT). However, to transmit sensor data from UIoT to remote onshore data processing center requires a huge cost of deploying and maintaining communication infrastructures. In this article, we propose an unmanned aerial vehicles (UAVs)-assisted underwater data acquisition scheme by placing multiple sink nodes on the water surface to serve as intermediate relays between underwater sensors (IoT nodes) and UAVs. In our scheme, the sensor data are first transmitted via an acoustic-signal link to a buoyant sink node, which then forwards the data to a UAV via an electromagnetic link. In particular, we adopt two sink-node-deployment methods, i.e., grid placement and random placement of sink nodes. Since the path connectivity from an underwater sensor node to the UAV is crucial to guarantee reliable data acquisition tasks, we establish a theoretical framework to analyze the path connectivity via the intermediate sink node for both grid and random sink-node-deployment methods. Extensive simulation results validate the accuracy of the proposed analytical model. Moreover, our results also reveal the relationship between the path connectivity and other factors, such as sink node placements, antenna beamwidth of UAVs, and wind speed. We also further extend our UAV-assisted data acquisition to other scenarios with the consideration of trajectories of UAVs, movements of sink nodes, interference of both underwater acoustic and terrestrial radio links, and integration with edge computing.

A pilot study of a wearable monitoring system as an adjunct to geriatric assessment in older adults with cancer.

2062 Background: Advances in health technology provide potential tools that can aid in assessing and monitoring the functional status of the growing older adult population diagnosed with cancer. We piloted a novel wearable monitoring platform, Sensing in At-Risk Populations (SARP), which consists of a smartwatch, software application for health monitoring, and a central data processing and analytics engine. Methods: This is a prospective single center, single arm study, utilizing the SARP platform to risk stratify older adults with cancer and determine correlation with treatment-related adverse events and healthcare utilization. Pts age ≥60 undergoing active treatment, were offered participation. Pts were instructed to wear the smartwatch for ≥7 days. We used Kruskal-Wallis to correlate wearable data with clinical outcomes: toxicity, ED visits, hospitalizations, and mortality. We also compared SARP data to independently collected ECOG PS, CARG score, ADLs, and IADLs. Results: From 8/2016 to 8/2017, 54 older adults were consented, and 26 had wearable data available for analysis. The average age was 72 years, with 18 males and 8 females. 12 pts had ECOG PS of 0, 12 with ECOG of 1, and 2 with ECOG of 2. 4 pts had CARG score of low, 17 intermediate, and 3 high. Energy intensity was significantly correlated with ED visits, with an effect size of 0.95 (p = 0.04). Similarly, energy intensity and hospitalizations had an effect size of 0.87 (p = 0.06). The CARG scores were noted to be significantly correlated with dose delay and dose reduction with an effect size 0.45 (p = 0.05) and 0.4 (p = 0.05), respectively. Spearman correlation analysis demonstrated that walking time, active time, and energy intensity positively correlate with ADLs and IADLs, and inversely correlated with ECOG PS and CARG risk. Conclusions: Though this is a limited study due to sample size, the overall trend demonstrated that the SARP platform offers an adjunct tool in assessing and risk stratifying older patients with cancer undergoing active therapy. Additional cohorts are now enrolled with an at-home monitoring system. [Table: see text]

Model Survei Cerdas Lalu Lintas Harian Rata-Rata (ADT) untuk Perencanaan dan Pemantauan Perkerasan

Survey volume of daily traffic for the planning and supervision of pavement road manually requires effort, time and cost is not cheap. Besides that, accuracy, and speed in getting the data are not timely, especially as real-time data. This study aims to get an intelligent survey model that will function to get daily traffic volume data automatically and in real-time, as well as its processing. The methodology used is to model the survey process by utilizing smart devices, microcontrollers, sensors, and the internet of things, as well as cloud and client-server programming. The survey process model can be divided into two parts, namely part one whose task is to collect vehicle data and part two to collect the axle load of each vehicle, then the data will be sent through an access point to the cloud, cloud data is taken from the access point at the data processing center on the server computer to done processing. The model generating information volume data traffic at speeds up to 67.4 Mbps, to accurately the data 98%, and the real-time and the results of data processing can be used as surveillance of pavement age.

Effect of Virtual Machine Deployment Policies on the Efficiency Data Processing Centers

The use of deployment policies with the development of virtual machines (VMs) allows improving the reliability of applications, reducing the latency in interactions between VMs, and simplifying the administration at data processing centers. Setting VM deployment policies places additional requirements on physical resources. This can result in reduced physical resource utilization and a lower percentage of deployed requests. The article proposes an algorithm for mapping requests (for creating virtual resources) for data centers’ (DCs) physical resources with the optional consideration of VM deployment policies and presents the results of an experimental study of the effect of deployment policies on the load on the physical resources and the percentage of deployed requests.