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Journal articles on the topic 'Heterogeneous distributed systems'

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1

Dullerud, G. E., and R. D'Andrea. "Distributed Control of Heterogeneous Systems." IEEE Transactions on Automatic Control 49, no. 12 (December 2004): 2113–28. http://dx.doi.org/10.1109/tac.2004.838499.

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2

Karatza, Helen D. "Job scheduling in heterogeneous distributed systems." Journal of Systems and Software 56, no. 3 (March 2001): 203–12. http://dx.doi.org/10.1016/s0164-1212(00)00098-4.

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3

Nessett, Dan M., and Geoffrey M. Lee. "Terminal Services in Heterogeneous distributed systems." Computer Networks and ISDN Systems 19, no. 2 (October 1990): 105–28. http://dx.doi.org/10.1016/0169-7552(90)90149-m.

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4

Barri, I., C. Roig, F. Giné, and F. Solsona. "Mapping MMOFPS over heterogeneous distributed systems." Journal of Supercomputing 58, no. 3 (March 15, 2011): 341–48. http://dx.doi.org/10.1007/s11227-011-0590-8.

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5

Puustjärvi, Juha. "Distributed management of transactions in heterogeneous distributed database systems." BIT 31, no. 3 (September 1991): 406–20. http://dx.doi.org/10.1007/bf01933259.

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6

Hu, Hanpeng, Dan Wang, and Chuan Wu. "Distributed Machine Learning through Heterogeneous Edge Systems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7179–86. http://dx.doi.org/10.1609/aaai.v34i05.6207.

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Many emerging AI applications request distributed machine learning (ML) among edge systems (e.g., IoT devices and PCs at the edge of the Internet), where data cannot be uploaded to a central venue for model training, due to their large volumes and/or security/privacy concerns. Edge devices are intrinsically heterogeneous in computing capacity, posing significant challenges to parameter synchronization for parallel training with the parameter server (PS) architecture. This paper proposes ADSP, a parameter synchronization model for distributed machine learning (ML) with heterogeneous edge systems. Eliminating the significant waiting time occurring with existing parameter synchronization models, the core idea of ADSP is to let faster edge devices continue training, while committing their model updates at strategically decided intervals. We design algorithms that decide time points for each worker to commit its model update, and ensure not only global model convergence but also faster convergence. Our testbed implementation and experiments show that ADSP outperforms existing parameter synchronization models significantly in terms of ML model convergence time, scalability and adaptability to large heterogeneity.
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7

A.Nasr, Aida, Nirmeen A. El-Bahnasawy, and AymanEl-Sayed AymanEl-Sayed. "Task Scheduling Optimization in Heterogeneous Distributed Systems." International Journal of Computer Applications 107, no. 4 (December 18, 2014): 5–12. http://dx.doi.org/10.5120/18737-9982.

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8

Alonso, A., J. C. Dueñas, G. León, Μ. de Miguel, and A. Rendón. "Heterogeneous Prototyping for Distributed Real-Time Systems." IFAC Proceedings Volumes 28, no. 22 (September 1995): 111–16. http://dx.doi.org/10.1016/s1474-6670(17)46678-x.

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9

Bhat, Prashanth B., C. S. Raghavendra, and Viktor K. Prasanna. "Efficient collective communication in distributed heterogeneous systems." Journal of Parallel and Distributed Computing 63, no. 3 (March 2003): 251–63. http://dx.doi.org/10.1016/s0743-7315(03)00008-x.

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10

Bhat, Prashanth B., Viktor K. Prasanna, and C. S. Raghavendra. "Adaptive Communication Algorithms for Distributed Heterogeneous Systems." Journal of Parallel and Distributed Computing 59, no. 2 (November 1999): 252–79. http://dx.doi.org/10.1006/jpdc.1999.1571.

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11

Thomas, Gomer, Glenn R. Thompson, Chin-Wan Chung, Edward Barkmeyer, Fred Carter, Marjorie Templeton, Stephen Fox, and Berl Hartman. "Heterogeneous distributed database systems for production use." ACM Computing Surveys 22, no. 3 (September 1990): 237–66. http://dx.doi.org/10.1145/96602.96607.

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12

Geihs, K., R. Heite, and U. H. Hollberg. "Protected object references in heterogeneous distributed systems." IEEE Transactions on Computers 42, no. 7 (July 1993): 809–16. http://dx.doi.org/10.1109/12.237721.

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13

Nicol, J. R., C. T. Wilkes, and F. A. Manola. "Object orientation in heterogeneous distributed computing systems." Computer 26, no. 6 (June 1993): 57–67. http://dx.doi.org/10.1109/2.214434.

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14

Ramesh, Akula, and Dia L. Ali. "Query transformation in heterogeneous distributed database systems." Computers & Industrial Engineering 31, no. 1-2 (October 1996): 323–26. http://dx.doi.org/10.1016/0360-8352(96)00141-6.

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15

Valk, Laurens, and Tamás Keviczky. "Distributed Control of Heterogeneous Underactuated Mechanical Systems." IFAC-PapersOnLine 51, no. 23 (2018): 325–30. http://dx.doi.org/10.1016/j.ifacol.2018.12.056.

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16

Lea, Rodger, and Jonathan Walpole. "Supporting object interaction in heterogeneous distributed systems." Computer Communications 13, no. 6 (July 1990): 365–73. http://dx.doi.org/10.1016/0140-3664(90)90059-p.

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17

Mirchandaney, Ravi, Don Towsley, and John A. Stankovic. "Adaptive load sharing in heterogeneous distributed systems." Journal of Parallel and Distributed Computing 9, no. 4 (August 1990): 331–46. http://dx.doi.org/10.1016/0743-7315(90)90118-9.

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18

Lim, Jong T., and Song C. Moon. "Global checkpointing scheme for heterogeneous distributed database systems." Microprocessing and Microprogramming 32, no. 1-5 (August 1991): 747–54. http://dx.doi.org/10.1016/0165-6074(91)90432-s.

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19

Kang, Sukhoon, and Songchun Moon. "Global query management in heterogeneous distributed database systems." Microprocessing and Microprogramming 38, no. 1-5 (September 1993): 377–84. http://dx.doi.org/10.1016/0165-6074(93)90170-p.

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20

Villar, Eugenio, Javier Merino, Hector Posadas, Rafik Henia, and Laurent Rioux. "Mega-modeling of complex, distributed, heterogeneous CPS systems." Microprocessors and Microsystems 78 (October 2020): 103244. http://dx.doi.org/10.1016/j.micpro.2020.103244.

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21

Radojevic, Ivan, Zoran Salcic, and Partha Roop. "Design of Distributed Heterogeneous Embedded Systems in DDFCharts." IEEE Transactions on Parallel and Distributed Systems 22, no. 2 (February 2011): 296–308. http://dx.doi.org/10.1109/tpds.2010.69.

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22

Leslie, Robert, and Sati McKenzie. "Evaluation of loadsharing algorithms for heterogeneous distributed systems." Computer Communications 22, no. 4 (March 1999): 376–89. http://dx.doi.org/10.1016/s0140-3664(98)00262-x.

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23

Canini, Marco, Vojin Jovanović, Daniele Venzano, Dejan Novaković, and Dejan Kostić. "Online testing of federated and heterogeneous distributed systems." ACM SIGCOMM Computer Communication Review 41, no. 4 (October 22, 2011): 434–35. http://dx.doi.org/10.1145/2043164.2018507.

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24

Kafil, M., and I. Ahmad. "Optimal task assignment in heterogeneous distributed computing systems." IEEE Concurrency 6, no. 3 (July 1998): 42–50. http://dx.doi.org/10.1109/4434.708255.

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25

Massioni, Paolo. "Distributed control for alpha-heterogeneous dynamically coupled systems." Systems & Control Letters 72 (October 2014): 30–35. http://dx.doi.org/10.1016/j.sysconle.2014.08.006.

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26

Ernvall, Toni, Salim El Rouayheb, Camilla Hollanti, and H. Vincent Poor. "Capacity and Security of Heterogeneous Distributed Storage Systems." IEEE Journal on Selected Areas in Communications 31, no. 12 (December 2013): 2701–9. http://dx.doi.org/10.1109/jsac.2013.131210.

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27

Falcone, Joseph R. "A programmable interface language for heterogeneous distributed systems." ACM Transactions on Computer Systems 5, no. 4 (October 1987): 330–51. http://dx.doi.org/10.1145/29868.29870.

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28

Chronopoulos, Anthony T., Satish Penmatsa, Jianhua Xu, and Siraj Ali. "Distributed loop-scheduling schemes for heterogeneous computer systems." Concurrency and Computation: Practice and Experience 18, no. 7 (2006): 771–85. http://dx.doi.org/10.1002/cpe.960.

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29

Gligor, Virgil, and Radu Popescu-Zeletin. "Transaction management in distributed heterogeneous database management systems." Information Systems 11, no. 4 (January 1986): 287–97. http://dx.doi.org/10.1016/0306-4379(86)90009-8.

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30

Goyal, M. L., and G. V. Singh. "Access control in distributed heterogeneous database management systems." Computers & Security 10, no. 7 (November 1991): 661–69. http://dx.doi.org/10.1016/0167-4048(91)90124-v.

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31

Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, Furqan Aziz, Bader Alouffi, M. Irfan Uddin, and Ahmed AlGhamdi. "Efficient Processing of Image Processing Applications on CPU/GPU." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/4839876.

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Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.
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32

Desai, BC, and R. Pollock. "MDAS: heterogeneous distributed database management system." Information and Software Technology 34, no. 1 (January 1992): 28–42. http://dx.doi.org/10.1016/0950-5849(92)90092-4.

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33

Jammalamadaka, Sastry Kodanda Rama, Valluru Sai Kumar Reddy, and Smt J Sasi Bhanu. "Networking Heterogeneous Microcontroller based Systems through Universal Serial Bus." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (October 1, 2015): 992. http://dx.doi.org/10.11591/ijece.v5i5.pp992-1002.

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Networking heterogeneous embedded systems is a challenge. Every distributed embedded systems requires that the network is designed specifically considering the heterogeneity that exits among different Microcontroller based systems that are used in developing a distributed embedded system. Communication architecture, which considers the addressing of the individual systems, arbitration, synchronisation, error detection and control etc., needs to be designed considering a specific application. The issue of configuring the slaves has to be addressed. It is also important that the messages, flow of the messages across the individual ES systems must be designed. Every distributed embedded system is different and needs to be dealt with separately. This paper presents an approach that addresses various issues related to networking distributed embedded systems through use of universal serial bus communication protocol (USB). The approach has been applied to design a distributed embedded that monitors and controls temperatures within a Nuclear reactor system.
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34

GhorbanniaDelavar, Arash, and Yalda Aryan. "A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems." International Journal of Computer Applications 52, no. 8 (August 30, 2012): 27–33. http://dx.doi.org/10.5120/8223-1656.

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35

Rus, Primož, Boris Štok, and Nikolaj Mole. "Parallel computing with load balancing on heterogeneous distributed systems." Advances in Engineering Software 34, no. 4 (April 2003): 185–201. http://dx.doi.org/10.1016/s0965-9978(02)00141-2.

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36

Lee, Wonsup, Haengrae Cho, and Songchun Moon. "Group-oriented catalog allocation in heterogeneous distributed database systems." Journal of Systems Architecture 43, no. 1-5 (March 1997): 267–76. http://dx.doi.org/10.1016/s1383-7621(96)00102-6.

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37

Kang, Sukhoon, and Songchun Moon. "An integrated access control in heterogeneous distributed database systems." Microprocessing and Microprogramming 35, no. 1-5 (September 1992): 429–36. http://dx.doi.org/10.1016/0165-6074(92)90350-g.

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38

Ontuzheva, G. A., E. R. Bruchanova, I. N. Rudov, N. O. Pikov, and O. A. Antamoshkin. "Simulation modelling of the heterogeneous distributed information processing systems." IOP Conference Series: Materials Science and Engineering 450 (November 30, 2018): 052018. http://dx.doi.org/10.1088/1757-899x/450/5/052018.

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39

Wang, Jin-Long. "Threshold-based load balancing policies in heterogeneous distributed systems." International Journal of Systems Science 30, no. 7 (January 1999): 729–42. http://dx.doi.org/10.1080/002077299292047.

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40

Tang, Xiaoyong, Kenli Li, Renfa Li, and Bharadwaj Veeravalli. "Reliability-aware scheduling strategy for heterogeneous distributed computing systems." Journal of Parallel and Distributed Computing 70, no. 9 (September 2010): 941–52. http://dx.doi.org/10.1016/j.jpdc.2010.05.002.

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41

Grasso, Ivan, Simone Pellegrini, Biagio Cosenza, and Thomas Fahringer. "A uniform approach for programming distributed heterogeneous computing systems." Journal of Parallel and Distributed Computing 74, no. 12 (December 2014): 3228–39. http://dx.doi.org/10.1016/j.jpdc.2014.08.002.

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42

Wen, Kai, and Zhiyong Geng. "Stability of distributed heterogeneous systems with static nonlinear interconnections." Systems & Control Letters 59, no. 11 (November 2010): 680–86. http://dx.doi.org/10.1016/j.sysconle.2010.08.004.

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43

Tiemeyer, Matthew P., and Johnny S. K. Wong. "A task migration algorithm for heterogeneous distributed computing systems." Journal of Systems and Software 41, no. 3 (June 1998): 175–88. http://dx.doi.org/10.1016/s0164-1212(97)10018-8.

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44

Drozdowski, M., and M. Lawenda. "Multi-installment divisible load processing in heterogeneous distributed systems." Concurrency and Computation: Practice and Experience 19, no. 17 (2007): 2237–53. http://dx.doi.org/10.1002/cpe.1180.

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45

Qiu, R. G., and M. C. Zhou. "Emerging Approaches to Integrating Distributed, Heterogeneous, and Complex Systems." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 36, no. 1 (January 2006): 2–4. http://dx.doi.org/10.1109/tsmca.2005.861466.

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46

Zhao, Huanyu, and Shumin Fei. "Distributed consensus for discrete-time heterogeneous multi-agent systems." International Journal of Control 91, no. 6 (April 13, 2017): 1376–84. http://dx.doi.org/10.1080/00207179.2017.1315650.

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47

Zhang, Xuxi, Xianping Liu, and Zhiguang Feng. "Distributed containment control of singular heterogeneous multi-agent systems." Journal of the Franklin Institute 357, no. 3 (February 2020): 1378–99. http://dx.doi.org/10.1016/j.jfranklin.2019.10.025.

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48

Zhang, Yanqiong, Zhenhua Deng, and Yiguang Hong. "Distributed optimal coordination for multiple heterogeneous Euler–Lagrangian systems." Automatica 79 (May 2017): 207–13. http://dx.doi.org/10.1016/j.automatica.2017.01.004.

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49

Zhang, C., and D. A. Bell. "HECODES: a framework for HEterogeneous COoperative distributed expert systems." Data & Knowledge Engineering 6, no. 3 (May 1991): 251–73. http://dx.doi.org/10.1016/0169-023x(91)90007-k.

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50

Pop, Florin, Alexandru Iosup, and Radu Prodan. "HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems." Future Generation Computer Systems 78 (January 2018): 242–44. http://dx.doi.org/10.1016/j.future.2017.09.012.

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