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4th Korea-Russia International Symposium on Science and Technology (KORUS 2000)

作者： Tsapko, G Grishmanovsky, P Danilenko, I Balakanov, A Tomsk Polytechn Univ Chair Automat & Comp Syst Cybernet Ctr Tomsk Russia

ISBN: (纸本)0780364864

The development of control systems often implies necessity of the organization of parallel computing processes or the implementation of software of distributed data processing. Solving this problems we should carry out such stages as constructing system structure, developing scheme of processes communication, choosing special language tools of the parallel programming. Such formal tools of simulation as Petri nets and its extensions can lighten the resolving of this problems. The communication of parallel processes is described by the net structure, which can be easily obtained from the process state graph and the. algorithm implemented by this process. It is important to note that Petri nets allow to describe asynchronous and parallel independent events. Occurrences of transitions act as events and result in creating, moving and deleting tokens. In this case the processes are represented by tokens, and the cortege of token attributes acts as a data segment of the process. To this effect the object-oriented modification of the hierarchical E-nets is developed. At the developing of the given E-nets modification we have been concentrated on the creating of the open program interface. By this we have achieved two important factors. First, ne can bind the net events and any calculations or input/output operations by any available code library. Second, constructed in that way the kernel of a distributed system is independent and can be used in a resulted software product. Though the development of the corresponding design tools are not finished, the effectiveness of the given approach is confirmed by the results of scientific work of authors.

关键词： distributed data processing simulation Petri nets parallel processes

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Optimization of distributed data processing System for NICA BM@N Experiment by Using Simulation 5th

Optimization of Distributed Data Processing System for NICA ...

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5th International Young Scientist Conference on Computational Science (YSC)

作者： Korenkov, Vladimir Nechaevskiy, Andrey Ososkov, Gennadiy Potrebenikov, Yuri Pryahina, Darya Trofimov, Vladimir Uzhinskiy, Alexander Joint Inst Nucl Res Dubna Russia Plekhanov Russian Univ Econ Moscow Russia

Superconducting accelerator complex NICA is now in a developing stage at the Joint Institute for Nuclear Research* (JINR). The development of the NICA computing complex is carried out at the JINR's Laboratory of Information Technologies (LIT) with the aim to process, analyze, store experimental data and simulate its sophisticated design and evolution. NICA includes the BM@N experiment first run of which is planned to be performed in 2017. Therefore, it is necessary to develop a computing system for distributed data storing and processing for the experiment. The simulation program SyMSim is used to choose a proper architecture of the BM@N computing system infrastructure. SyMSim facilitates making a decision regarding a required equipment. The paper presents the results obtained by simulating a process of receiving and processing data from the BM@N experiment involving a model of the computing complex.

关键词： NICA project computing distributed data processing simulation

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GIS spatial data partitioning method for distributed data processing

GIS spatial data partitioning method for distributed data pr...

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Symposium on Remote Sensing and GIS data processing and Applications;and Innovative Multispectral Technology and Applications

作者： Zhou, Yan Zin, Qing Zhang, Yeting Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)9780819469540

Spatial data partitioning strategy plays an important role in GIS spatial data distributed storage and processing, its key problem is how to partition spatial data to distributed nodes in network environment. Existing main spatial data partitioning methods doesn't consider spatial locality and unstructured variable length characteristics of spatial data, these methods simply partition spatial data based on one or more attributes value that could result in storage capacity imbalance between distributed processing nodes. Aiming at these, we point out the two basic principles that spatial data partitioning should meet to in this paper. We propose a new spatial data partitioning method based on hierarchical decomposition method of low order Hilbert space-filling curve, which could avoid excessively intensive space partitioning by hierarchically decomposing subspaces. The proposed method uses Hilbert curve to impose a linear ordering on the multidimensional spatial objects, and partition the spatial objects according to this ordering. Experimental results show the proposed spatial data partitioning method not only achieves better storage load balance between distributed nodes, but also keeps well spatial locality of data objects after partitioning.

关键词： spatial data partitioning Hilbert space-filling curve hierarchical decomposition distributed data processing

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Agent-based computational architectures for distributed data processing in wireless sensor networks

Agent-based computational architectures for distributed data...

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作者： Zimmerman, Andrew T. University of Michigan

学位级别：Ph.D.

As the structural health monitoring (SHM) community continues to develop algorithms for monitoring performance and detecting degradation in engineered systems, the importance of pervasive sensing and autonomous data processing methodologies will increase. Fortunately, the emergence of wireless sensor technologies at the forefront of SHM research has provided a platform on which problems related to both sensor density and processing autonomy can be addressed. By utilizing wireless communication links instead of expensive data cables, wireless monitoring systems can be deployed with much greater sensor density and at significantly lower costs than traditional SHM systems. Perhaps more importantly, because wireless sensing units typically integrate a traditional sensor with a low-power microprocessor, analog-to-digital converter, and wireless transceiver, wireless sensing networks (WSNs) have shown great promise in their ability to process sensor data in-network (i.e., without the need for a centralized data center). Over the past decade, the wireless SHM community has shown that it is possible to minimize problems associated with power efficiency, data loss, and finite communication range by processing data before transmitting it to a central repository. Recently, in an effort to further improve the efficiency and capability of in-network computation, researchers have started to move away from centralized processing frameworks (where no data is shared between nodes) towards more hierarchical data processing architectures. However, work to date in this area has yet to fully leverage the computational advantages provided in large networks of wireless sensors. In this dissertation, several distinct agent-based architectures are developed for distributed data processing in WSNs. Each of these agent-based architectures leverages the ad-hoc communication and pervasive nature inherent to wireless sensing technology, and can be viewed as a parallel computing system with an un

关键词： Wireless Sensors Wireless Sensing Networks Agent-Based Systems distributed data processing Structural Health Monitoring Thesis

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Green IoT Edge Computing Towards Sustainable and distributed data processing 10

Green IoT Edge Computing Towards Sustainable and Distributed...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Ananth, K.R. Punna, Hari Shankar Selvaraj, Krishnamoorthy Rajagopal, K. Mahajan, Vaishali Sakthivel, S. Tamilnadu Erode India Department of Cse Telangana India Department of Erp Keller India Maharashtra Pune India K.S.Rangasamy College of Arts and Science Department of Computer Applications Tamilnadu Tiruchengode India

ISBN: (纸本)9798350382471

data creation in the network's periphery has increased exponentially as IoT devices have become more commonplace. To address the challenges this large data deluge poses, sustainable and efficient data processing technologies are required. The term 'Green IoT Edge Computing' refers to a new strategy that uses distributed data processing and renewable energy to improve the efficiency and longevity of Internet of Things (IoT) applications. In the Internet of Things (IoT), edge computing is used to process data locally, at the network's periphery, rather than forwarding it to a centralized data center. This method improves real-time decision-making, minimizes network latency, and helps preserve bandwidth. However, in locations with limited access to power or other resources, the high power consumption of edge computing devices continues to be a major issue. Green IoT edge computing addresses this challenge by combining renewable energy sources like solar panels and wind turbines with traditional edge computing facilities. This guarantees low- impact and long-term power for edge devices, cutting down on their environmental impact and operational expenses. This strategy harmonizes IoT operations with environmental sustainability targets by utilizing renewable energy sources. The ability to handle data in a distributed manner is also crucial to green IoT edge computing. Rather than a centralized server, a distributed group of edge devices processes data. This improves energy efficiency as well as fault tolerance and dependability. Edge devices working together to properly spread processing activities can reduce the demand for powerful, energy-hungry servers. Green IoT Edge Computing's primary invention is the 'Renewable Edge Optimizer.' This clever piece of code automatically adjusts data processing tasks in accordance with the availability of renewable power. When clean energy is plentiful, the optimizer may shift more data processing to distributed nodes, relieving press

关键词： distributed data processing Edge Computing etc Green IoT Renewable Edge Optimizer Renewable Energy Sustainability

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GF-3 data real-time processing method based on multi-satellite distributed data processing system

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Journal of Central South University 2020年第3期27卷 842-852页

作者： YANG Jun CAO Yan-dong SUN Guang-cai XING Meng-dao GUO Liang College of Geomatics Xi’an University of Science and TechnologyXi’an 710000China National Laboratory of Radar Signal Processing Xidian UniversityXi’an 710071China School of Physics and Optoelectronic Engineering Xidian UniversityXi’an 710071China

Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not *** of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture *** is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the *** from the traditional CS algorithm processing,the system divides data processing into three *** computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each *** method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing ***-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.

关键词： synthetic aperture radar full-track utilization rate distributed data processing CS imaging algorithm field programmable gate array Gaofen-3

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Optimization of distributed data processing System for NICA [email protected] Experiment by Using Simulation

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Procedia Computer Science 2016年 101卷 333-340页

作者： Vladimir Korenkov Andrey Nechaevskiy Gennadiy Ososkov Yuri Potrebenikov Darya Pryahina Vladimir Trofimov Alexander Uzhinskiy Joint Institute for Nuclear Research Dubna Russian Federation Plekhanov Russian University of Economics Moscow Russian Federation

Superconducting accelerator complex NICA is now in a developing stage at the Joint Institute for Nuclear Research 1 (JINR). The development of the NICA computing complex is carried out at the JINR's Laboratory of Information Technologies (LIT) with the aim to process, analyze, store experimental data and simulate its sophisticated design and evolution. NICA includes the [email protected] experiment first run of which is planned to be performed in 2017. Therefore, it is necessary to develop a computing system for distributed data storing and processing for the experiment. The simulation program SyMSim is used to choose a proper architecture of the [email protected] computing system infrastructure. SyMSim facilitates making a decision regarding a required equipment. The paper presents the results obtained by simulating a process of receiving and processing data from the [email protected] experiment involving a model of the computing complex.

关键词： NICA project computing distributed data processing simulation

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Configuration and supervision of advanced distributed data acquisition and processing systems for long pulse experiments using JINI technology

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FUSION ENGINEERING AND DESIGN 2009年第2-6期84卷 832-836页

作者： Gonzalez, Joaquin Ruiz, Mariano Barrera, Eduardo Manuel Lopez, Juan de Arcas, Guillermo Vega, Jesus Univ Politecn Madrid Grp Invest Instrumentac & Acust Aplicada Madrid 28031 Spain EURATOM CIEMAT Fus Madrid 28040 Spain

The development of tools for managing the capabilities and functionalities of distributed data acquisition systems is essential in long pulse fusion experiments. The intelligent test and measurement system (ITMS) developed by UPM and CIEMAT is a technology that permits implementation of a scalable data acquisition and processing system based on PXI or CompactPCI hardware. Several applications based on JINI technology have been developed to enable use of this platform for extensive implementation of distributed data acquisition and processing systems. JINI provides a framework for developing service-oriented, distributed applications. The applications are based on the paradigm of a JINI federation that supports mechanisms for publication, discovering, subscription, and links to remote services. The model we implemented in the ITMS platform included services in the system CPU (SCPU) and peripheral CPUs (PCPUs). The resulting system demonstrated the following capabilities: (1) setup of the data acquisition and processing to apply to the signals, (2) information about the evolution of the data acquisition, (3) information about the applied data processing and (4) detection and distribution of the events detected by the ITMS software applications. With this approach, software applications running on the ITMS platform can be understood, from the perspective of their implementation details, as a set of dynamic, accessible, and transparent services. The search for services is performed using the publication and subscription mechanisms of the JINI specification. The configuration and supervision applications were developed using remotely accessible (LAN or WAN) objects. The consequence of this approach is a hardware and software architecture that provides a transparent model of remote configuration and Supervision, and thereby a means to simplify the implementation of a distributed data acquisition system with scalable and dynamic local processing capability developed in a f

关键词： JINI Long pulse distributed data processing Remote participation

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Optimizing distributed data stream processing by tracing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 90卷 578-591页

作者： Zvara, Zoltan Szabo, Peter G. N. Balazs, Barnabas Benczur, Andras Hungarian Acad Sci Inst Comp Sci & Control MTA SZTAKI 13-17 Kende U H-1111 Budapest Hungary

Heterogeneous mobile, sensor, loT, smart environment, and social networking applications have recently started to produce unbounded, fast, and massive-scale streams of data that have to be processed "on the fly". Systems that process such data have to be enhanced with detection for operational exceptions and with triggers for both automated and manual operator actions. In this paper, we illustrate how tracing in distributed data processing systems can be applied to detecting changes in data and operational environment to maintain the efficiency of heterogeneous data stream processing systems under potentially changing data quality and distribution. By the tracing of individual input records, we can (1) identify outliers in a web crawling and document processing system and use the insights to define URL filtering rules;(2) identify heavy keys, such as NULL, that should be filtered before processing;(3) give hints to improve the key-based partitioning mechanisms;and (4) measure the limits of overpartitioning if heavy thread-unsafe libraries are imported. By using Apache Spark as illustration, we show how various data stream processing efficiency issues can be mitigated or optimized by our distributed tracing engine. We describe and qualitatively compare two different designs, one based on reporting to a distributed database and another based on trace piggybacking. Our prototype implementation consists of wrappers suitable for JVM environments in general, with minimal impact on the source code of the core system. Our tracing framework is the first to solve tracing in multiple systems across boundaries and to provide detailed performance measurements suitable for automated optimization, not just debugging. (C) 2018 Elsevier B.V. All rights reserved.

关键词： distributed data processing data stream processing distributed tracing data provenance Apache Spark

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PXI-based architecture for real-time data acquisition and distributed dynamic data processing

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2006年第3期53卷 923-926页

作者： Barrera, E Ruiz, M López, S Machón, D Vega, J Tech Univ Madrid Elect & Control Syst Dept Madrid Spain Tech Univ Madrid Instrumentat & Appl Acoust Res Grp Madrid Spain EURATOM CIEMAT Assoc Fus Data Acquisit Grp Madrid Spain

This paper describes an architecture model for data acquisition systems based on compact PC! platforms. The aim is to increase real-time data processing capabilities,in experimental environments such as nuclear fusion devices (e.g., ITER). The model has these features: 1) Real-time data acquisition: the system has been provided with real-time capabilities, developing specific data acquisition kernel modules under Linux and RTAI, using COMEDI project drivers;2) Multiprocessor PCI eXtensions for Instrumentation (PXI) Architecture: the model makes possible to add one or more processing cards (in nonsystem slots) to each standard PXI chassis. Several real-time software modules have been developed to allow the communication between the PXI controller and the processing cards. This way the system performance is not restricted to the PXI controller's own performance. This model provides scalability,to the system, adding or removing processing cards;3) Real-time acquired data distribution: with this model it is possible to define how to distribute, in real-time, the data from all acquired signals in the system among the processing cards and the PXI controller;and 4) Dynamic data processing: a software platform has been developed to allow users to dynamically manage their own data processing algorithms in the system. This means that users can start, stop, modify, and replace their data processing algorithms without disrupting the data acquisition process or the rest of the data processing algorithms.

关键词： data acquisition distributed data processing real-time systems thermonuclear fusion

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