Journal articles: 'Distributed applications'

1

Black, Andrew P. "Supporting distributed applications." ACM SIGOPS Operating Systems Review 19, no. 5 (December 1985): 181–93. http://dx.doi.org/10.1145/323627.323646.

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Roper, Matthew D., and Ronald A. Olsson. "Application-specific thread schedulers for distributed applications." Concurrency and Computation: Practice and Experience 24, no. 3 (September 21, 2011): 260–80. http://dx.doi.org/10.1002/cpe.1821.

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Burdette, Steven, Tracy Camp, and Bill Bynum. "Distributed BACI: a toolkit for distributed applications." Concurrency: Practice and Experience 12, no. 1 (January 2000): 35–52. http://dx.doi.org/10.1002/(sici)1096-9128(200001)12:1<35::aid-cpe458>3.0.co;2-8.

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4

Challis, J. "Review: Distributed Applications Engineering." Computer Bulletin 41, no. 1 (January 1, 1999): 30–31. http://dx.doi.org/10.1093/combul/41.1.30-b.

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5

Phuah, Vincent, Jose Diaz-Gonzalez, Russ Sasnett, and Steve Gutfreund. "Developing distributed multimedia applications." ACM SIGCOMM Computer Communication Review 22, no. 3 (July 1992): 57–60. http://dx.doi.org/10.1145/142267.142307.

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Hoppen, Martin, Ralf Waspe, Malte Rast, and Juergen Rossmann. "Distributed Information Processing and Rendering for 3D Simulation Applications." International Journal of Computer Theory and Engineering 6, no. 3 (2014): 247–53. http://dx.doi.org/10.7763/ijcte.2014.v6.870.

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7

Chaib-draa, B. "Industrial applications of distributed AI." Communications of the ACM 38, no. 11 (November 1995): 49–53. http://dx.doi.org/10.1145/219717.219761.

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Menasce, D. A. "Allocating applications in distributed computing." IEEE Internet Computing 9, no. 1 (January 2005): 90–92. http://dx.doi.org/10.1109/mic.2005.8.

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Stillerman, Matthew, Carla Marceau, and Maureen Stillman. "Intrusion detection for distributed applications." Communications of the ACM 42, no. 7 (July 1999): 62–69. http://dx.doi.org/10.1145/306549.306577.

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10

Rofrano,, J. J. "Design considerations for distributed applications." IBM Systems Journal 31, no. 3 (1992): 564–89. http://dx.doi.org/10.1147/sj.313.0564.

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11

Ratnikova, N., A. Sciaba, and S. Wynhoff. "Distributing applications in distributed environment." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 502, no. 2-3 (April 2003): 458–60. http://dx.doi.org/10.1016/s0168-9002(03)00469-8.

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Chabernaud, C., and B. Vilain. "Telecommunication services and distributed applications." IEEE Network 4, no. 6 (November 1990): 10–13. http://dx.doi.org/10.1109/65.60731.

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13

Bubak, Marian, Włodzimierz Funika, Roland Wismüller, Piotr Mętel, and Rafał Orłowski. "Monitoring of distributed Java applications." Future Generation Computer Systems 19, no. 5 (July 2003): 651–63. http://dx.doi.org/10.1016/s0167-739x(02)00175-9.

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14

Williams, Neil, and Gordon S Blair. "Distributed multimedia applications: A review." Computer Communications 17, no. 2 (February 1994): 119–32. http://dx.doi.org/10.1016/s0140-3664(05)80016-7.

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15

Haridi, Seif, Peter Van Roy, Per Brand, and Christian Schulte. "Programming languages for distributed applications." New Generation Computing 16, no. 3 (September 1998): 223–61. http://dx.doi.org/10.1007/bf03037481.

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Urnes, Tore, Arne S. Hatlen, Pål S. Malm, and Øystein Myhre. "Building Distributed Context-Aware Applications." Personal and Ubiquitous Computing 5, no. 1 (February 28, 2001): 38–41. http://dx.doi.org/10.1007/s007790170027.

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Coveney, P. V., G. De Fabritiis, M. J. Harvey, S. M. Pickles, and A. R. Porter. "Coupled applications on distributed resources." Computer Physics Communications 175, no. 6 (September 2006): 389–96. http://dx.doi.org/10.1016/j.cpc.2006.05.008.

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18

Dickman, Peter. "Objects in large distributed applications." ACM SIGPLAN OOPS Messenger 3, no. 4 (September 1992): 91–95. http://dx.doi.org/10.1145/143776.143800.

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19

Egelhaaf, C., E. Moeller, and P. Schoo. "Developing distributed multimedia telecommunication applications." IEEE Multimedia 3, no. 4 (1996): 76–81. http://dx.doi.org/10.1109/93.556463.

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Vin, H. M. "Supporting next-generation distributed applications." IEEE Multimedia 5, no. 3 (1998): 78–83. http://dx.doi.org/10.1109/93.713309.

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Bacon, J., K. Moody, J. Bates, Chaoying Ma, A. McNeil, O. Seidel, and M. Spiteri. "Generic support for distributed applications." Computer 33, no. 3 (March 2000): 68–76. http://dx.doi.org/10.1109/2.825698.

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22

Raynal, Michel. "Distributed Computability." ACM SIGACT News 52, no. 2 (June 14, 2021): 92–110. http://dx.doi.org/10.1145/3471469.3471484.

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As today Computer Science is more and more (driven) consumed by its applications, it becomes more and more important to know what is and what is not computable. For a long time now, this has been relatively well understood in sequential computing. But today the computing world becomes more and more distributed, and consequently more and more applications are distributed. As a result, it becomes important, or even crucial, to understand what is distributed computing and which are its power and its limits. This article is a step in this direction from an agreement-oriented and fault-tolerance perspective.

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23

Prince, Daryl. "Distributed Control." Mechanical Engineering 121, no. 01 (January 1, 1999): 68–69. http://dx.doi.org/10.1115/1.1999-jan-6.

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This article discusses servo motion systems, which are motion control systems that combine hardware and software, have innumerable applications in compact modules. Some motion controllers operate on multiple platforms and buses, with units providing analog output to a conventional amplifier, as well as units that provide current control and direct pulse width modulation (PWM) output for as many as 32 motors simultaneously. There are amplifiers that still require potentiometers to be adjusted for the digital drives’ position, velocity, and current control. All major value-adding components of motion control systems will soon have to comply with the demands for faster controllers with high-speed multi axis capabilities supplying commands in multitasking applications.

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24

Liu, Kaikai, Nagthej Manangi Ravindrarao, Abhishek Gurudutt, Tejeshwar Kamaal, Chinmayi Divakara, and Praveen Prabhakaran. "Software-Defined Edge Cloud Framework for Resilient Multitenant Applications." Wireless Communications and Mobile Computing 2019 (January 1, 2019): 1–17. http://dx.doi.org/10.1155/2019/3947286.

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Edge application’s distributed nature presents significant challenges for developers in orchestrating and managing the multitenant applications. In this paper, we propose a practical edge cloud software framework for deploying multitenant distributed smart applications. Here we exploit commodity, a low cost embedded board to form distributed edge clusters. The cluster of geo-distributed and wireless edge nodes not only power multitenant IoT applications that are closer to the data source and the user, but also enable developers to remotely deploy and orchestrate application containers over the cloud. Specifically, we propose building a software platform to manage the distributed edge nodes along with support services to deploy and launch isolated and multitenant user applications through a lightweight container. In particular, we propose an architectural solution to improve the resilience of edge cloud services through peer collaborated service migration when the failures happen or when resources are overburdened. We focus on giving the developers a single point control of the infrastructure over the intermittent and lossy wide area networks (WANs) and enabling the remote deployment of multitenant applications.

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25

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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26

Górski, Tomasz. "Continuous Delivery of Blockchain Distributed Applications." Sensors 22, no. 1 (December 25, 2021): 128. http://dx.doi.org/10.3390/s22010128.

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Ensuring a production-ready state of the application under development is the imminent feature of the Continuous Delivery (CD) approach. In a blockchain network, nodes communicate and store data in a distributed manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research focusing on continuous practices for blockchain and Distributed Ledger Technology (DLT). Specifically, it lacks such works with support for both design and deployment. The author has proposed a solution that takes into account the continuous delivery of a business application to diverse deployment environments in the DLT network. As a result, two continuous delivery pipelines have been implemented using the Jenkins automation server. The first pipeline prepares a business application whereas the second one generates complete node deployment packages. As a result, the framework ensures the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The Smart Contract Design Pattern has been used when building a business application. The modeling aspect of blockchain network installation has required using Unified Modeling Language (UML) and the UML Profile for Distributed Ledger Deployment. The refined model-to-code transformation generates deployment configurations for nodes. Both the business application and deployment configurations are stored in the GitHub repositories. For the sake of verification, tests have been conducted for the electricity consumption and supply management system designed for prosumers of renewable energy.

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27

Sinopoli, B., C. Sharp, L. Schenato, S. Schaffert, and S. S. Sastry. "Distributed control applications within sensor networks." Proceedings of the IEEE 91, no. 8 (August 2003): 1235–46. http://dx.doi.org/10.1109/jproc.2003.814926.

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28

Handerek, V. "Distributed vibration sensing: Principles and applications." Journal of Physics: Conference Series 1065 (August 2018): 252026. http://dx.doi.org/10.1088/1742-6596/1065/25/252026.

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29

Manolescu, Dragos, Brian Beckman, and Benjamin Livshits. "Volta: Developing Distributed Applications by Recompiling." IEEE Software 25, no. 5 (September 2008): 53–59. http://dx.doi.org/10.1109/ms.2008.131.

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30

Plale, B., G. Eisenhauer, K. Schwan, J. Heiner, V. Martin, and J. Vetter. "From interactive applications to distributed laboratories." IEEE Concurrency 6, no. 2 (April 1998): 78–90. http://dx.doi.org/10.1109/4434.678828.

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31

Brook, Andrew. "Low-latency distributed applications in finance." Communications of the ACM 58, no. 7 (June 25, 2015): 42–50. http://dx.doi.org/10.1145/2747303.

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32

Poggi, Agostino, and Michele Tomaiuolo. "Multilanguage Semantic Interoperability in Distributed Applications." Journal of Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/182525.

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JOSI is a software framework that tries to simplify the development of such kinds of applications both by providing the possibility of working on models for representing such semantic information and by offering some implementations of such models that can be easily used by software developers without any knowledge about semantic models and languages. This software library allows the representation of domain models through Java interfaces and annotations and then to use such a representation for automatically generating an implementation of domain models in different programming languages (currently Java and C++). Moreover, JOSI supports the interoperability with other applications both by automatically mapping the domain model representations into ontologies and by providing an automatic translation of each object obtained from the domain model representations in an OWL string representation.

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33

Ste˛pien´, Pawel̸. "Distributed kinoforms in optical security applications." Optical Engineering 35, no. 9 (September 1, 1996): 2453. http://dx.doi.org/10.1117/1.600847.

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34

Bergenti, Federico, Eleonora Iotti, Agostino Poggi, and Michele Tomaiuolo. "Concurrent and Distributed Applications with ActoDeS." MATEC Web of Conferences 76 (2016): 04043. http://dx.doi.org/10.1051/matecconf/20167604043.

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35

Tesoriero, Ricardo, and María Dolores Lozano. "Distributed User Interfaces: Applications and Challenges." International Journal of Human-Computer Interaction 28, no. 11 (November 2012): 697–99. http://dx.doi.org/10.1080/10447318.2012.715048.

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36

Geer, D. "In brief: IPv6 and distributed applications." IEEE Distributed Systems Online 6, no. 12 (2005): 4. http://dx.doi.org/10.1109/mdso.2005.65.

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37

Baude, Francoise, Denis Caromel, and David Sagnol. "Distributed Objects for Parallel Numerical Applications." ESAIM: Mathematical Modelling and Numerical Analysis 36, no. 5 (September 2002): 837–61. http://dx.doi.org/10.1051/m2an:2002039.

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38

Wong, J. W., K. A. Lyons, D. Evans, R. J. Velthuys, G. v. Bochmann, E. Dubois, N. D. Georganas, et al. "Enabling technology for distributed multimedia applications." IBM Systems Journal 36, no. 4 (1997): 489–507. http://dx.doi.org/10.1147/sj.364.0489.

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39

Montijano, Eduardo, Juan Ignacio Montijano, and Carlos Sagues. "Chebyshev Polynomials in Distributed Consensus Applications." IEEE Transactions on Signal Processing 61, no. 3 (February 2013): 693–706. http://dx.doi.org/10.1109/tsp.2012.2226173.

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40

Lamboray, Edouard, Aaron Zollinger, Oliver G. Staadt, and Markus Gross. "Interactive multimedia streams in distributed applications." Computers & Graphics 27, no. 5 (October 2003): 735–45. http://dx.doi.org/10.1016/s0097-8493(03)00147-x.

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41

Pehrson, B., P. Gunningberg, and S. Pink. "Distributed multimedia applications on gigabit networks." IEEE Network 6, no. 1 (January 1992): 26–35. http://dx.doi.org/10.1109/65.120721.

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42

Lan, Zhiling, Valerie E. Taylor, and Greg Bryan. "Exploring cosmology applications on distributed environments." Future Generation Computer Systems 19, no. 6 (August 2003): 839–47. http://dx.doi.org/10.1016/s0167-739x(03)00064-5.

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43

Katchabaw, Michael J., Stephen L. Howard, Hanan L. Lutfiyya, Andrew D. Marshall, and Michael A. Bauer. "Making distributed applications manageable through instrumentation." Journal of Systems and Software 45, no. 2 (March 1999): 81–97. http://dx.doi.org/10.1016/s0164-1212(98)10070-5.

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44

Kotsis, Gabriele, and Markus Braun. "Graph based characterization of distributed applications." Future Generation Computer Systems 16, no. 6 (April 2000): 597–607. http://dx.doi.org/10.1016/s0167-739x(99)00076-x.

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45

Hadellis, Loukas V., and Vassilios D. Kapsalis. "Distributed Control Network for Agricultural Applications." IFAC Proceedings Volumes 31, no. 12 (June 1998): 337–42. http://dx.doi.org/10.1016/s1474-6670(17)36087-1.

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46

Cavendish, Dirceu, and K. Selçuk Candan. "Distributed XML processing: Theory and applications." Journal of Parallel and Distributed Computing 68, no. 8 (August 2008): 1054–69. http://dx.doi.org/10.1016/j.jpdc.2008.04.003.

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47

Chadwick, David. "Coordinated decision making in distributed applications." Information Security Technical Report 12, no. 3 (2007): 147–54. http://dx.doi.org/10.1016/j.istr.2007.05.003.

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48

Shepherd, Doug, David Hutchinson, Francisco Garcia, and Geoff Coulson. "Protocol support for distributed multimedia applications." Computer Communications 15, no. 6 (July 1992): 359–66. http://dx.doi.org/10.1016/0140-3664(92)90010-c.

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49

Shangyou Hao, A. Papalexopoulos, and Tie-Mao Peng. "Distributed processing for contingency screening applications." IEEE Transactions on Power Systems 10, no. 2 (May 1995): 838–44. http://dx.doi.org/10.1109/59.387924.

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50

Shi, Yang, Jiahu Qin, and Hyo-Sung Ahn. "Distributed Coordination Control and Industrial Applications." IEEE Transactions on Industrial Electronics 64, no. 6 (June 2017): 4967–71. http://dx.doi.org/10.1109/tie.2017.2665318.

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Journal articles on the topic 'Distributed applications'

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