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Journal articles on the topic 'Distributed data system'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

K., Dhinakaran. "Distributed Data Analytics for Improving Indian Economical Growth Using Recommendation System." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 134–40. http://dx.doi.org/10.5373/jardcs/v12sp4/20201474.

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2

Paul Beattie, W., and Lance R. Bailey. "A distributed data entry system." Controlled Clinical Trials 10, no. 3 (September 1989): 343. http://dx.doi.org/10.1016/0197-2456(89)90155-4.

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3

Liu, Ying, Jiarong Luo, Guiming Li, Yingfei Zhu, and Shi Li. "The EAST distributed data system." Fusion Engineering and Design 82, no. 4 (June 2007): 339–43. http://dx.doi.org/10.1016/j.fusengdes.2007.02.014.

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4

Butner, David N., Marena Drlik, William H. Meyer, Jeffrey M. Moller, and George G. Preckshot. "Integrated, multivendor distributed data‐acquisition system." Review of Scientific Instruments 59, no. 8 (August 1988): 1786–88. http://dx.doi.org/10.1063/1.1140112.

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5

NAKANO, Tsukasa, and Naoyuki FUJII. "Processing System for Two-dimensionally Distributed Data : (2) Interpolation for Irregularly Distributed Data." Geological data processing 1989, no. 14B (1989): 113–32. http://dx.doi.org/10.6010/geoinformatics1975.1989.14b_113.

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6

Hall, I. K., and P. D. Stigall. "Distributed flight control system using fiber distributed data interface (FDDI)." IEEE Aerospace and Electronic Systems Magazine 7, no. 6 (June 1992): 21–33. http://dx.doi.org/10.1109/62.145116.

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7

O., Orlunwo Placida, and Prince Oghenekaro A. "DISTRIBUTED DATABASE MANAGEMENT SYSTEM (DBMS) ARCHITECTURES AND DISTRIBUTED DATA INDEPENDENCE." International Journal of Computer Science and Mobile Computing 10, no. 1 (January 28, 2021): 23–48. http://dx.doi.org/10.47760/ijcsmc.2021.v10i01.004.

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8

Leong, K. K., K. M. Yu, and W. B. Lee. "Product data allocation for distributed product data management system." Computers in Industry 47, no. 3 (March 2002): 289–98. http://dx.doi.org/10.1016/s0166-3615(01)00152-x.

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9

Grzybowski, Piotr, Maciej Klimczuk, and Pawel Rzucidlo. "Distributed measurement system based on CAN data bus." Aircraft Engineering and Aerospace Technology 90, no. 8 (November 5, 2018): 1249–58. http://dx.doi.org/10.1108/aeat-11-2017-0247.

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Purpose This paper aims to describe the idea behind and design of a miniaturized distributed measurement system based on a controller area network (CAN) data bus. Design/methodology/approach The intention of the designers was to build a light and modular measurement system which can be used in remotely piloted aircraft systems and ultra-light aircraft during flight tests, as well as normal operation. The structure of this distributed measurement system is based on a CAN data bus. The CAN aerospace standard has been applied to the software as well as the hardware comprising this system. PRP-W2 software designed for PCs is an additional component of the proposed measurement system. This software supports data acquisition from a recorder unit and allows for preliminary data analysis, as well as data conversion and presentation. Findings The system, complete with a high-speed data recorder, was successfully installed on board of an MP-02 Czajka aircraft. A research experiment using the system and oriented on airframe high frequency vibration analysis is presented in the final part of this paper. Research limitations/implications This measurement system allows analysis of high-frequency vibrations occurring at selected points of the aircraft. A data set is recorded by three-axis accelerometers and gyroscopes at frequencies up to 1 kHz. Practical implications The use of a miniature and lightweight modular measurement system will, in many cases, be faster and less expensive than full-scale measurement and data acquisition systems, which often require a lengthy assembly process. The implementation of this class of lightweight flight test systems has many advantages, in particular to the operation of small aircraft. Such solutions are likely to become increasingly common in unmanned aerial vehicles and in other light aircraft in the future. Originality/value The adaptation of a distributed measuring system with a high frequency of measurements for purposes of small and miniature aircraft.

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10

Shin, Ji Eun, Byung Ho Jung, and Dong Hoon Lim. "Big data distributed processing system using RHadoop." Journal of the Korean Data and Information Science Society 26, no. 5 (September 30, 2015): 1155–66. http://dx.doi.org/10.7465/jkdi.2015.26.5.1155.

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11

Wu, Shu Guang, and Jian Xiang Chen. "Design of Distributed Pulse Data Acquisition System." Advanced Materials Research 756-759 (September 2013): 756–59. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.756.

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The design of the wireless distributive data acquisition system has been presented based on MSP430 embedded microprocessor and nRF24C01 module for the native mobile telemedicine. The focus of portable electronic products is Low Power Consumption and accuracy of data acquisition. Pulse data acquisition has been simulated with Self-adapting Selection operation, and the data provides a reliable gist for the prevention, treatment, diagnosis and therapeutic efficacy of cardiovascular disease.

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12

Zhou, Ting, and Amedeo Caflisch. "Data Management System for Distributed Virtual Screening." Journal of Chemical Information and Modeling 49, no. 1 (January 26, 2009): 145–52. http://dx.doi.org/10.1021/ci800295q.

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13

Manhood, S. J., I. Jenkins, and J. Waterhouse. "The MAST data acquisition system — distributed implementation." Fusion Engineering and Design 48, no. 1-2 (August 2000): 219–23. http://dx.doi.org/10.1016/s0920-3796(00)00131-9.

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14

Ricardo, R., B. Miguel, G. Benjamin, G. Vincent, L. Mario, S. Pedro, and C. David. "Monitoring the atlas distributed data management system." Journal of Physics: Conference Series 119, no. 7 (July 1, 2008): 072027. http://dx.doi.org/10.1088/1742-6596/119/7/072027.

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15

Li, Peng, Xi Fang, Zi Peng Chen, and Zhuo Qiu Li. "Wireless Distributed Data Acquisition System for EIT." Applied Mechanics and Materials 490-491 (January 2014): 1298–301. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1298.

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In recently years, Electrical Impedance Tomography caused concern for people in the areas such as non-destructive testing and structural health monitoring. This paper took the disk structure composed of carbon fiber felt for example, designed and built the experiment platform for Electrical Impedance Tomography in a couple of different ways: one was traditional and the other was distributed acquisition system based on wireless sensor network. The imaging result showed that the data collected from wireless distributed system were more accurate and efficient.

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16

Daly, R. T., M. R. Kraimer, and A. H. Novick. "Neutral Particle Beam Distributed Data Acquisition System." IEEE Transactions on Nuclear Science 34, no. 4 (1987): 816–21. http://dx.doi.org/10.1109/tns.1987.4334743.

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17

Barisits, Martin, Vincent Garonne, Mario Lassnig, and Angelos Molfetas. "Simulating the ATLAS Distributed Data Management System." Journal of Physics: Conference Series 396, no. 5 (December 13, 2012): 052009. http://dx.doi.org/10.1088/1742-6596/396/5/052009.

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18

Watson, W. A., J. Chen, G. Heyes, E. Jastrzembski, and D. Quarrie. "CODA: a scalable, distributed data acquisition system." IEEE Transactions on Nuclear Science 41, no. 1 (1994): 61–68. http://dx.doi.org/10.1109/23.281458.

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19

Hang, Guo, and Yu Min. "Data fusion in distributed multi-sensor system." Geo-spatial Information Science 7, no. 3 (January 2004): 214–17. http://dx.doi.org/10.1007/bf02826294.

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20

Fukuda, Hiroaki, Ryota Gunji, Tadahiro Hasegawa, Paul Leger, and Ismael Figueroa. "DSSM: Distributed Streaming Data Sharing Manager." Sensors 21, no. 4 (February 14, 2021): 1344. http://dx.doi.org/10.3390/s21041344.

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Developing robot control software systems is difficult because of a wide variety of requirements, including hardware systems and sensors, even though robots are demanding nowadays. Middleware systems, such as Robot Operating System (ROS), are being developed and widely used to tackle this difficulty. Streaming data Sharing Manager (SSM) is one of such middleware systems that allow developers to write and read sensor data with timestamps using a Personal Computer (PC). The timestamp feature is essential for the robot control system because it usually uses multiple sensors with their own measurement cycles, meaning that measured sensor values with different timestamps become useless for the robot control. Using SSM allows developers to use measured sensor values with the same timestamps; however, SSM assumes that only one PC is used. Thereby, if one process consumes CPU resources intensively, other processes cannot finish their assumed deadlines, leading to the unexpected behavior of a robot. This paper proposes an SSM middleware, named Distributed Streaming data Sharing Manager (DSSM), that enables distributing processes on SSM to different PCs. We have developed a prototype of DSSM and confirmed its behavior so far. In addition, we apply DSSM to an existing real SSM based robot control system that autonomously controls an unmanned vehicle robot. We then reveal its advantages and disadvantages via several experiments by measuring resource usages.

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21

Hussain, G. Fayaz, and Tarakeswar T. "File Systems and Hadoop Distributed File System in Big Data." IJARCCE 5, no. 12 (December 30, 2016): 36–40. http://dx.doi.org/10.17148/ijarcce.2016.51207.

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22

Ma, T. H., W. Tian, B. Wang, D. H. Guan, and S. Y. Lee. "Weather data sharing system: an agent-based distributed data management." IET Software 5, no. 1 (2011): 21. http://dx.doi.org/10.1049/iet-sen.2009.0027.

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23

Lakshmi Siva Rama Krishna, T., J. Priyanka, N. Nikhil Teja, Sd Mahiya Sultana, and B. Jabber. "An Efficient Data Replication Scheme for Hadoop Distributed File System." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 167. http://dx.doi.org/10.14419/ijet.v7i2.32.15396.

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A Distributed file system (DFS) is a storage component of a distributed system (DS). DS consists of multiple autonomous nodes connected via a communication network to solve large problems and to achieve more computing power. One of the design requirement of any DS is to provide replicas. In this paper, we propose a new replication algorithm which is more reliable than the existing replication algorithm used in DFS. The advantages of our proposed replication algorithm by incrementing nodes sequentially (RAINS) is that it distributes the storage load equally among all the nodes sequentially and it guarantees a replica copy in case two racks in a DS are down. This feature is not available in the existing DFS. We have compared existing replication algorithm used by Hadoop distributed file system (HDFS) with our proposed RAINS algorithm. The experimental results indicate that our proposed RAINS algorithm performs better when more number of racks failed in the DS.

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24

Valeev, S. S., N. V. Kondratyeva, A. S. Kovtunenko, M. A. Timirov, and R. R. Karimov. "Distributed stream data processing system in multi-agent safety system of infrastructure objects." Information Technology and Nanotechnology, no. 2416 (2019): 324–31. http://dx.doi.org/10.18287/1613-0073-2019-2416-324-331.

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The solution of the problem of resource management in distributed computing systems of processing stream data in safety systems of distributed objects is considered. The tasks of streaming data processing in a multi-level multi-agent evacuation system in an infrastructure object are considered. The features of the mathematical model of a distributed stream data processing system are discussed.

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25

Fontana, Cristiano L., Alberto Carnera, Marcello Lunardon, Felix E. Pino, Cinzia Sada, Francesca Soramel, Luca Stevanato, and Sandra Moretto. "A distributed data acquisition system for nuclear detectors." International Journal of Modern Physics: Conference Series 48 (January 2018): 1860118. http://dx.doi.org/10.1142/s2010194518601187.

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Nowadays, many examples of data acquisition (DAQ) software for experimental nuclear physics are monolithic processes that run on a computer attached to the DAQ hardware. In this article we present a distributed DAQ system developed for the C-BORD project. With our system, we propose a novel approach, in which each task related to the different DAQ parts (acquisition, pre-process, analysis, etc.) runs in a separate process. In particular, the system is composed of a set of servers that exchange information through dedicated communication sockets. Therefore, with this architecture, an important advantage is the possibility to run the processes on different computers to distribute the computational load. The initial tests of the system have been giving excellent results, both in terms of performance (i.e., maximum acquisition rates) and stability. The project entitled “Effective container inspection at BORDer control points” (C-BORD) is funded by the European H2020 programme. Its aim is to develop a comprehensive set of technologies for the generalized non-intrusive inspection (NII) of containers and large-volume freight at the European Union border.

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26

Khoruzhnikov, S. E., and A. Ye Shevel. "Management system for scalable geographically distributed data center." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 19, no. 5 (October 1, 2019): 931–38. http://dx.doi.org/10.17586/2226-1494-2019-19-5-931-938.

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27

Fujita, M., Y. Kurihara, F. Ueno, S. Kano, H. Nakajima, N. Yokoyama, and S. Iwata. "Distributed Database for Materials (Data-Free-Way system)." Proceedings of Annual Conference, Japan Society of Information and Knowledge 2 (1994): 39–42. http://dx.doi.org/10.2964/jsikproc.2.0_39.

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28

Agarwal, Manu, and Gaurav Agarwal. "Accessing the Data Security Model in Distributed System." International Journal of Applied Information Systems 1, no. 4 (February 18, 2012): 11–14. http://dx.doi.org/10.5120/ijais12-450157.

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29

Jeon, Seungwoo, Bonghee Hong, Joonho Kwon, Yoon-sik Kwak, and Seok-il Song. "Design of Distributed Sensor Stream Data Input System." International Journal of Smart Home 7, no. 5 (September 30, 2013): 39–48. http://dx.doi.org/10.14257/ijsh.2013.7.5.04.

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30

Huang, Chi-Sheng, Meng-Feng Tsai, Po-Hsuan Huang, Li-Ding Su, and Kuei-Sheng Lee. "Distributed asteroid discovery system for large astronomical data." Journal of Network and Computer Applications 93 (September 2017): 27–37. http://dx.doi.org/10.1016/j.jnca.2017.03.013.

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31

Gaiazov, S. E., V. S. Banzarov, F. V. Ignatov, I. B. Logashenko, S. A. Pirogov, A. M. Sukharev, and A. S. Zaytsev. "Distributed data analysis system for CMD-3 detector." Journal of Instrumentation 9, no. 08 (August 20, 2014): C08011. http://dx.doi.org/10.1088/1748-0221/9/08/c08011.

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32

Coleman, J. J., C. B. Meltzer, and J. L. Weiner. "Fiber Distributed Data Interface attachment to System/390." IBM Journal of Research and Development 36, no. 4 (July 1992): 647–54. http://dx.doi.org/10.1147/rd.364.0647.

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33

Rossman, Paul, and the Cms Computing and Offline Projects. "Ensuring data consistency over CMS distributed computing system." Journal of Physics: Conference Series 219, no. 7 (April 1, 2010): 072050. http://dx.doi.org/10.1088/1742-6596/219/7/072050.

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34

LIN, XIN, WEIHUI SHI, and YONGXIANG DU. "INTERACTIVE DATA ENCRYPTION STRATEGY FOR DISTRIBUTED SIMULATION SYSTEM." International Journal of Modeling, Simulation, and Scientific Computing 04, no. 04 (September 12, 2013): 1342005. http://dx.doi.org/10.1142/s1793962313420051.

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The distributed simulation system interoperation can be divided into six levels. Interactive data encryption can be completed in each level, lead to six encryption strategies: data field encryption, data package encryption, program module encryption, simulation application encryption, simulation node encryption, and simulation system encryption. There are four basic Encryption/decryption realization modes: serial modes with software or hardware realization, parallel modes based on embedded processor or FPGA/ASIC system. Large and Complex distributed simulation system may employ one or several encryption strategies and realization modes.

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35

Kim, Yeongho, Suk-Ho Kang, Soo-Hong Lee, and Sang Bong Yoo. "A distributed, open, intelligent product data management system." International Journal of Computer Integrated Manufacturing 14, no. 2 (January 2001): 224–35. http://dx.doi.org/10.1080/09511920150216341.

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36

Perrizo, William. "A distributed tactical battle-data transaction management system." Mathematical and Computer Modelling 14 (1990): 1129–33. http://dx.doi.org/10.1016/0895-7177(90)90353-o.

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37

Zheng, W., Q. Liu, M. Zhang, K. Wan, F. Hu, and K. Yu. "J-TEXT distributed data storage and management system." Fusion Engineering and Design 129 (April 2018): 207–13. http://dx.doi.org/10.1016/j.fusengdes.2018.02.058.

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38

Tahmazli-Khaligova, Firuza. "CHALLENGES OF USING BIG DATA IN DISTRIBUTED EXASCALE SYSTEMS." Azerbaijan Journal of High Performance Computing 3, no. 2 (December 29, 2020): 245–54. http://dx.doi.org/10.32010/26166127.2020.3.2.245.254.

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In a traditional High Performance Computing system, it is possible to process a huge data volume. The nature of events in classic High Performance computing is static. In Distributed Exa-scale System has a different nature. The processing Big data in a distributed exascale system evokes a new challenge. The dynamic and interactive character of a distributed exascale system changes processes status and system elements. This paper discusses the challenge that Big data attributes: volume, velocity, variety, how they influence distributed exascale system dynamic and interactive nature. While investigating the effect of the Dynamic and Interactive nature of exascale systems in computing Big data, this research work suggests the Markov chains model. This model suggests the transition matrix, which identifies system status and memory sharing. It lets us analyze the two systems convergence. As a result in both systems are explored by the influence of each other.

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39

WICKE, DANIEL. "DATA REPROCESSING ON WORLDWIDE DISTRIBUTED SYSTEMS." International Journal of Modern Physics A 20, no. 16 (June 30, 2005): 3880–82. http://dx.doi.org/10.1142/s0217751x05027874.

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The DØ experiment faces many challenges in terms of enabling access to large datasets for physicists on four continents. The strategy for solving these problems on worldwide distributed computing clusters is presented. Since the beginning of Run II of the Tevatron (March 2001) all Monte-Carlo simulations for the experiment have been produced at remote systems. For data analysis, a system of regional analysis centers (RACs) was established which supply the associated institutes with the data. This structure, which is similar to the tiered structure foreseen for the LHC was used in Fall 2003 to reprocess all DØ data with a much improved version of the reconstruction software. This makes DØ the first running experiment that has implemented and operated all important computing tasks of a high energy physics experiment on systems distributed worldwide.

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40

Sherstnyov, Vladislav S., Anna I. Sherstnyova, Igor A. Botygin, and Denis A. Kustov. "Distributed Information System for Processing and Storage of Meteorological Data." Key Engineering Materials 685 (February 2016): 867–71. http://dx.doi.org/10.4028/www.scientific.net/kem.685.867.

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The following article features the results of developing distributed network storage of ground meteorological observation data. The data is represented with the national variant of international rapid transmission code of environment data from meteorological stations across the Russian Federation. They are available for researchers in both visual and common export formats. The design of the distributed network storage of meteorological data includes the following modules: dispatcher module (monitors calculation nodes, distributes data to nodes, processes client requests), client module (allows external researchers to access the meteorological data), terminal module (used to import new meteorological data), data processing and storage module (a node for distributed meteorological data storage, consists of two sub-modules for data processing and data storage respectively). The article displays the results of practical testing of the developed software. To simulate the cluster of informational and calculation servers in the pilot project, multithreading was used. Multithreading is supported by nearly every operational system for parallel data processing. The development tools chosen for the network storage allowed to design storage module interaction with the optimal efficiency, to ensure proper performance, stability and reliability of processing and managing large amounts of data. The obtained results allow using the designs for efficient management of meteorological surface observation data, for rapid data gathering, for systematization and storage of hydro-meteorological data in different alphanumeric codes and other related categories.

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41

Emara, Tamer Z., and Joshua Zhexue Huang. "A distributed data management system to support large-scale data analysis." Journal of Systems and Software 148 (February 2019): 105–15. http://dx.doi.org/10.1016/j.jss.2018.11.007.

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42

Lu, Paul. "Integrating Bulk-Data Transfer into the Aurora Distributed Shared Data System." Journal of Parallel and Distributed Computing 61, no. 11 (November 2001): 1609–32. http://dx.doi.org/10.1006/jpdc.2001.1758.

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43

Hauf, Steffen, Burkhard Heisen, Steve Aplin, Marijan Beg, Martin Bergemann, Valerii Bondar, Djelloul Boukhelef, et al. "The Karabo distributed control system." Journal of Synchrotron Radiation 26, no. 5 (August 9, 2019): 1448–61. http://dx.doi.org/10.1107/s1600577519006696.

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The Karabo distributed control system has been developed to address the challenging requirements of the European X-ray Free Electron Laser facility, including complex and custom-made hardware, high data rates and volumes, and close integration of data analysis for distributed processing and rapid feedback. Karabo is a pluggable, distributed application management system forming a supervisory control and data acquisition environment as part of a distributed control system. Karabo provides integrated control of hardware, monitoring, data acquisition and data analysis on distributed hardware, allowing rapid control feedback based on complex algorithms. Services exist for access control, data logging, configuration management and situational awareness through alarm indicators. The flexible framework enables quick response to the changing requirements in control and analysis, and provides an efficient environment for development, and a single interface to make all changes immediately available to operators and experimentalists.

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Vijayalakshmi, A., and C. John Paul. "Big Data Health Care System Using Distributed Wearable Sensors." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 429. http://dx.doi.org/10.14419/ijet.v7i4.10.21033.

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The modern world facilitates a luxurious life with more comfort using technology support. The latest developments not only support the human life, but it causes problems to the humans in many ways. The primary concern of Big Data is underway in Healthcare unit. Alerting before occurring of problem is evolved with the help of big data health care solutions. Different types of sensors can be integrated with big data to produce a good solution. Our system provides a mechanism for health care systems using hidden Markov model. It acts as an intelligent system. It provides appropriate support and forwarding health related data to do real time analysis for analyzing and understanding health status.

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45

Tiendrebeogo, Telesphore, and Mamadou Diarra. "Big Data Storage System Based on a Distributed Hash Tables System." International Journal of Database Management Systems 12, no. 5 (October 30, 2020): 1–9. http://dx.doi.org/10.5121/ijdms.2020.12501.

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The Big Data is unavoidable considering the place of the digital is the predominant form of communication in the daily life of the consumer. The control of its stakes and the quality its data must be a priority in order not to distort the strategies arising from their treatment in the aim to derive profit. In order to achieve this, a lot of research work has been carried out companies and several platforms created. MapReduce, is one of the enabling technologies, has proven to be applicable to a wide range of fields. However, despite its importance recent work has shown its limitations. And to remedy this, the Distributed Hash Tables (DHT) has been used. Thus, this document not only analyses the and MapReduce implementations and Top-Level Domain (TLD)s in general, but it also provides a description of a model of DHT as well as some guidelines for the planification of the future research.

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46

Li, Jun, Changsen Pan, and Menghan Lu. "A Seismic Data Processing System based on Fast Distributed File System." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 5 (April 6, 2015): 5779–88. http://dx.doi.org/10.24297/ijct.v14i5.3986.

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Big data has attracted an increasingly number of attentions with the advent of the cloud era, and in the field of seismic exploration, the amount of data created by seismic exploration has also experienced an incredible growth in order to satisfy the social needs. In this case, it is necessary to build a highly-effective system of data storage and process. In our paper, we aim at the properties of the seismic data and the requirement to the performance of IO, and establish a distributed file system with the goal of processing seismic data based on the Fast Distributed File System (Fast DFS), then test our system through a series of operations such as file write and read, and the results show that our file system is very proper and effective when processing seismic data.

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47

DESHIMARU, MEGUMI, SATOSHI TSURUTA, HIDEO TOGASHI, KOUJI AOKI, and MASAHIRO NAKANO. "Drug Information System Using Distributed Data Processing System Based on UNIX." Japanese Journal of Hospital Pharmacy 21, no. 6 (1995): 511–18. http://dx.doi.org/10.5649/jjphcs1975.21.511.

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48

Preckshot, George G., David N. Butner, Michael D. Brown, and William H. Meyer. "Transparent data access in a multivendor, distributed data‐acquisition and data processing system." Review of Scientific Instruments 59, no. 8 (August 1988): 1774–76. http://dx.doi.org/10.1063/1.1140108.

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49

Et. al., S. SriGowthem,. "Prediction Management from distributed data in Peer-to-Peer Distributed Network Systems." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 557–60. http://dx.doi.org/10.17762/turcomat.v12i1s.1929.

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Unnamed attributes of peer-to-peer (P2P) systems display them to abhorrent movement. Peer-to-peer (P2P) systems are bargain acclimated in “file-swapping” networks to abutment broadcast agreeable split. A bulk of P2P networks for book administration accept has been developed and locate. Its affiliate tender a ample summary of P2P accretion and attention on adequate administration networks andscientific .construct and acceptance a part of aeon can abate attacks by abhorrent peers. Its cardboard provides broadcast algorithms acclimated by a associate to acumen concerning believability of any other method on the accessible bounded advice which are includes accomplished communication and direction accustomed by others. Aeon plan calm to authorize assurance a part of anniversary added after application a priori report. Assurance commitment are flexible to changes in assurance a part of peers system.

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A., Botchkaryov. "STRUCTURAL ADAPTATION OF DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS USING REINFORCEMENT LEARNING METHODS." Computer systems and network 2, no. 1 (March 23, 2017): 13–26. http://dx.doi.org/10.23939/csn2020.01.013.

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Abstract:

A method of structural adaptation of data collection processes has been developed based on reinforcement learning of the decision block on the choice of actions at the structural and functional level subordinated to it, which provides a more efficient distribution of measuring and computing resources, higher reliability and survivability of information collection subsystems of an autonomous distributed system compared to methods of parametric adaptation. In particular, according to the results of experimental studies, the average amount of information collected in one step using the method of structural adaptation is 23.2% more than in the case of using the methods of parametric adaptation. At the same time, the amount of computational costs for the work of the structural adaptation method is on average 42.3% more than for the work of parametric adaptation methods. The reliability of the work of the method of structural adaptation was studied using the efficiency preservation coefficient for different values of the failure rate of data collection processes. Using the recovery rate coefficient for various values of relative simultaneous sudden failures, the survivability of a set of data collection processes organized by the method of structural adaptation has been investigated. In terms of reliability, the structural adaptation method exceeds the parametric adaptation methods by an average of 21.1%. The average survivability rate for the method of structural adaptation is greater than for methods of parametric adaptation by 18.4%. Key words: autonomous distributed system, data collection process, structural adaptation, reinforcement learning

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