Relevant bibliographies by topics / Heterogeneous distributed systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Heterogeneous distributed systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Heterogeneous distributed systems"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Heterogeneous distributed systems"

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Rizvanovic, Larisa. "Resource Management Framework for Distributed Heterogeneous Systems." Licentiate thesis, Västerås : School of Innovation, Design and Engineering [Akademin för innovation, design och teknik], Mälardalen University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-585.

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Dlodlo, Nomusa. "Heterogeneous distributed systems and user interface issues." Thesis, Liverpool John Moores University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361536.

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Eriksson, Lennart. "ROBUST COMMUNICATION IN HETEROGENEOUS AND DISTRIBUTED SYSTEMS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35828.

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This thesis work has aimed to implement a robust communication system for military aircrafts produced by Saab. A big part of the thesis focuses on a comparison study of different possible designs and their impact on the system. From the comparison study a decentralized, Peer to Peer, Publish/Subscribe system was selected for implementation. All publications are sent directly from a publisher to the subscribers without any intermediate forwarding node. This has shown good results in comparison to the previous centralized solution where all data had to pass through a single server node. The new system has one master node that is responsible for registrations of both publishers and subscribers and distribute any necessary information to affected clients. This thesis has shown that the Single Point of Failure that was one of the main issues on the previous design has been removed and the performance of the system has increased as well. The Round Trip Time of a set of messages has shown to be improved up to 70.78%.
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Hines, Kenneth J. "Coordination-centric debugging for heterogeneous distributed embedded systems /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6914.

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Bhasker, Bharat. "Query processing in heterogeneous distributed database management systems." Diss., Virginia Tech, 1992. http://hdl.handle.net/10919/39437.

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The goal of this work is to present an advanced query processing algorithm formulated and developed in support of heterogeneous distributed database management systems. Heterogeneous distributed database management systems view the integrated data through an uniform global schema. The query processing algorithm described here produces an inexpensive strategy for a query expressed over the global schema. The research addresses the following aspects of query processing: (1) Formulation of a low level query language to express the fundamental heterogeneous database operations; (2) Translation of the query expressed over the global schema to an equivalent query expressed over a conceptual schema; (3) An estimation methodology to derive the intermediate result sizes of the database operations; (4) A query decomposition algorithm to generate an efficient sequence of the basic database operations to answer the query. This research addressed the first issue by developing an algebraic query language called cluster algebra. The cluster algebra consists of the following operations: (a) Selection, union, intersection and difference, which are extensions of their relational algebraic counterparts to heterogeneous databases; (b) Normal-join and normal-projection which replace their counterparts, join and projection, in the relational algebra; (c) Two new operators embed and unembed to restructure the database schema. The second issue of the query translation was addressed by development of an algorithm that translates a cluster algebra query expressed over the virtual views to an equivalent cluster algebra query expressed over the conceptual databases. A non-parametric estimation methodology to estimate the result size of a cluster algebra operation was developed to address the third issue described above. Finally, this research developed a query decomposition algorithm, applicable to the relational and non-relational databases, that decomposes a query by computing all profitable semi-join operations, followed by the determination of the best sequence of join operations per processing site. The join optimization is performed by formulating a zero-one integer linear program that uses the non-parametric estimation technique to compute the sizes of intermediate results. The query processing algorithm was implemented in the context of DAVID, a heterogeneous distributed database management system.
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Raman, Pirabhu. "GEMS Gossip-Enabled Monitoring Service for heterogeneous distributed systems /." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000598.

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Athauda, Rukshan Indika. "Integration and querying of heterogeneous, autonomous, distributed database systems." FIU Digital Commons, 2000. http://digitalcommons.fiu.edu/etd/1332.

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Today, databases have become an integral part of information systems. In the past two decades, we have seen different database systems being developed independently and used in different applications domains. Today's interconnected networks and advanced applications, such as data warehousing, data mining & knowledge discovery and intelligent data access to information on the Web, have created a need for integrated access to such heterogeneous, autonomous, distributed database systems. Heterogeneous/multidatabase research has focused on this issue resulting in many different approaches. However, a single, generally accepted methodology in academia or industry has not emerged providing ubiquitous intelligent data access from heterogeneous, autonomous, distributed information sources. This thesis describes a heterogeneous database system being developed at Highperformance Database Research Center (HPDRC). A major impediment to ubiquitous deployment of multidatabase technology is the difficulty in resolving semantic heterogeneity. That is, identifying related information sources for integration and querying purposes. Our approach considers the semantics of the meta-data constructs in resolving this issue. The major contributions of the thesis work include: (i.) providing a scalable, easy-to-implement architecture for developing a heterogeneous multidatabase system, utilizing Semantic Binary Object-oriented Data Model (Sem-ODM) and Semantic SQL query language to capture the semantics of the data sources being integrated and to provide an easy-to-use query facility; (ii.) a methodology for semantic heterogeneity resolution by investigating into the extents of the meta-data constructs of component schemas. This methodology is shown to be correct, complete and unambiguous; (iii.) a semi-automated technique for identifying semantic relations, which is the basis of semantic knowledge for integration and querying, using shared ontologies for context-mediation; (iv.) resolutions for schematic conflicts and a language for defining global views from a set of component Sem-ODM schemas; (v.) design of a knowledge base for storing and manipulating meta-data and knowledge acquired during the integration process. This knowledge base acts as the interface between integration and query processing modules; (vi.) techniques for Semantic SQL query processing and optimization based on semantic knowledge in a heterogeneous database environment; and (vii.) a framework for intelligent computing and communication on the Internet applying the concepts of our work.
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Pop, Traian. "Analysis and Optimisation of Distributed Embedded Systems with Heterogeneous Scheduling Policies." Doctoral thesis, Linköping : Department of Computer and Information Science, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8934.

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Pàmies, Juárez Lluís. "On the design and optimization of heterogeneous distributed storage systems." Doctoral thesis, Universitat Rovira i Virgili, 2011. http://hdl.handle.net/10803/38881.

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Durant la última dècada, la demanda d’emmagatzematge de dades ha anat creixent exponencialment any rere any. Apart de demanar més capacitat d’emmagatzematge, el usuaris actualment també demanen poder accedir a les seves dades des de qualsevol lloc i des de qualsevol dispositiu. Degut a aquests nous requeriments, els usuaris estan actualment movent les seves dades personals (correus electrònics, documents, fotografies, etc.) cap a serveis d’emmagatzematge en línia com ara Gmail, Facebook, Flickr o Dropbox. Malauradament, aquests serveis d’emmagatzematge en línia estan sostinguts per unes grans infraestructures informàtiques que poques empreses poden finançar. Per tal de reduir el costs d’aquestes grans infraestructures informàtiques, ha sorgit una nova onada de serveis d’emmagatzematge en línia que obtenen grans infraestructures d’emmagatzematge a base d’integrar els recursos petits centres de dades, o fins i tot a base d’integrar els recursos d’emmagatzematge del usuaris finals. No obstant això, els recursos que formen aquestes noves infraestructures d’emmagatzematge són molt heterogenis, cosa que planteja un repte per al dissenyadors d’aquests sistemes: Com es poden dissenyar sistemes d’emmagatzematge en línia, fiables i eficients, quan la infraestructura emprada és tan heterogènia? Aquesta tesis presenta un estudi dels principals problemes que sorgeixen quan un vol respondre a aquesta pregunta. A més proporciona diferents eines per tal d’optimitzar el disseny de sistemes d’emmagatzematge distribuïts i heterogenis. Les principals contribucions són: Primer, creem un marc d’anàlisis per estudiar els efectes de la redundància de dades en el cost dels sistemes d’emmagatzematge distribuïts. Donat un esquema de redundància específic, el marc d’anàlisis presentat permet predir el cost mitjà d’emmagatzematge i el cost mitjà de comunicació d’un sistema d’emmagatzematge implementat sobre qualsevol infraestructura informàtica distribuïda. Segon, analitzem els impactes que la redundància de dades té en la disponibilitat de les dades, i en els temps de recuperació. Donada una redundància, i donat un sistema d’emmagatzematge heterogeni, creem un grup d’algorismes per a determinar la disponibilitat de les dades esperada, i els temps de recuperació esperats. Tercer, dissenyem diferents polítiques d’assignació de dades per a diferents sistemes d’emmagatzematge. Diferenciem entre aquells escenaris on la totalitat de la infraestructura està administrada per una sola organització, i els escenaris on diferents parts auto administrades contribueixen els seus recursos. Els objectius de les nostres polítiques d’assignació de dades són: (i) minimitzar la redundància necessària, (ii) garantir la equitat entre totes les parts que participen al sistema, i (iii) incentivar a les parts perquè contribueixin els seus recursos al sistema.
Over the last decade, users’ storage demands have been growing exponentially year over year. Besides demanding more storage capacity and more data reliability, today users also demand the possibility to access their data from any location and from any device. These new needs encourage users to move their personal data (e.g., E-mails, documents, pictures, etc.) to online storage services such as Gmail, Facebook, Flickr or Dropbox. Unfortunately, these online storage services are built upon expensive large datacenters that only a few big enterprises can afford. To reduce the costs of these large datacenters, a new wave of online storage services has recently emerged integrating storage resources from different small datacenters, or even integrating user storage resources into the provider’s storage infrastructure. However, the storage resources that compose these new storage infrastructures are highly heterogeneous, which poses a challenging problem to storage systems designers: How to design reliable and efficient distributed storage systems over heterogeneous storage infrastructures? This thesis provides an analysis of the main problems that arise when one aims to answer this question. Besides that, this thesis provides different tools to optimize the design of heterogeneous distributed storage systems. The contribution of this thesis is threefold: First, we provide a novel framework to analyze the effects that data redundancy has on the storage and communication costs of distributed storage systems. Given a generic redundancy scheme, the presented framework can predict the average storage costs and the average communication costs of a storage system deployed over a specific storage infrastructure. Second, we analyze the impacts that data redundancy has on data availability and retrieval times. For a given redundancy and a heterogeneous storage infrastructure, we provide a set of algorithms that allow to determine the expected data availability and expected retrieval times. Third, we design different data assignment policies for different storage scenarios. We differentiate between scenarios where the entire storage infrastructure is managed by the same organization, and scenarios where different parties contribute their storage resources. The aims of our assignment policies are: (i) to minimize the required redundancy, (ii) to guarantee fairness among all parties, and (iii) to encourage different parties to contribute their local storage resources to the system.
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Al-Sinayyid, Ali. "JOB SCHEDULING FOR STREAMING APPLICATIONS IN HETEROGENEOUS DISTRIBUTED PROCESSING SYSTEMS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1868.

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The colossal amounts of data generated daily are increasing exponentially at a never-before-seen pace. A variety of applications—including stock trading, banking systems, health-care, Internet of Things (IoT), and social media networks, among others—have created an unprecedented volume of real-time stream data estimated to reach billions of terabytes in the near future. As a result, we are currently living in the so-called Big Data era and witnessing a transition to the so-called IoT era. Enterprises and organizations are tackling the challenge of interpreting the enormous amount of raw data streams to achieve an improved understanding of data, and thus make efficient and well-informed decisions (i.e., data-driven decisions). Researchers have designed distributed data stream processing systems that can directly process data in near real-time. To extract valuable information from raw data streams, analysts need to create and implement data stream processing applications structured as a directed acyclic graphs (DAG). The infrastructure of distributed data stream processing systems, as well as the various requirements of stream applications, impose new challenges. Cluster heterogeneity in a distributed environment results in different cluster resources for task execution and data transmission, which make the optimal scheduling algorithms an NP-complete problem. Scheduling streaming applications plays a key role in optimizing system performance, particularly in maximizing the frame-rate, or how many instances of data sets can be processed per unit of time. The scheduling algorithm must consider data locality, resource heterogeneity, and communicational and computational latencies. The latencies associated with the bottleneck from computation or transmission need to be minimized when mapped to the heterogeneous and distributed cluster resources. Recent work on task scheduling for distributed data stream processing systems has a number of limitations. Most of the current schedulers are not designed to manage heterogeneous clusters. They also lack the ability to consider both task and machine characteristics in scheduling decisions. Furthermore, current default schedulers do not allow the user to control data locality aspects in application deployment.In this thesis, we investigate the problem of scheduling streaming applications on a heterogeneous cluster environment and develop the maximum throughput scheduler algorithm (MT-Scheduler) for streaming applications. The proposed algorithm uses a dynamic programming technique to efficiently map the application topology onto a heterogeneous distributed system based on computing and data transfer requirements, while also taking into account the capacity of underlying cluster resources. The proposed approach maximizes the system throughput by identifying and minimizing the time incurred at the computing/transfer bottleneck. The MT-Scheduler supports scheduling applications that are structured as a DAG, such as Amazon Timestream, Google Millwheel, and Twitter Heron. We conducted experiments using three Storm microbenchmark topologies in both simulated and real Apache Storm environments. To evaluate performance, we compared the proposed MT-Scheduler with the simulated round-robin and the default Storm scheduler algorithms. The results indicated that the MT-Scheduler outperforms the default round-robin approach in terms of both average system latency and throughput.
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Books on the topic "Heterogeneous distributed systems"

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Boualem, Benatallah, and Elmagarmid Ahmed K, eds. Interconnecting heterogeneous information systems. Boston: Kluwer, 1998.

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Xie, Guoqi, Gang Zeng, Renfa Li, and Keqin Li. Scheduling Parallel Applications on Heterogeneous Distributed Systems. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6557-7.

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Tkach, Itshak, and Yael Edan. Distributed Heterogeneous Multi Sensor Task Allocation Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34735-2.

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Zheng, Xiaohu. Load sharing in large-scale, heterogeneous distributed systems. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1992.

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Peter, Reiher, and Popek Gerald, eds. Conductor: Distributed adaptation for heterogeneous networks. Boston: Kluwer Academic Publishers, 2002.

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Pop, Traian. Analysis and optimisation of distributed embedded systems with heterogeneous scheduling policies. Linköping: Department of Computer and Information Science, Linköpings universitet, 2007.

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Pop, Traian. Scheduling and optimisation of heterogeneous time/event-triggered distributed embedded systems. Linko ping: Univ., 2003.

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Ian, O'Connor, Piguet Christian, and SpringerLink (Online service), eds. Design Technology for Heterogeneous Embedded Systems. Dordrecht: Springer Science+Business Media B.V., 2012.

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Pratt, S. J. Design of homogeneous distributed operating systems for use within heterogeneous processor configurations. Portsmouth: Portsmouth Polytechnic, School of Information Science, 1989.

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Marinos, Loudovikos. A corporate architecture and object oriented modeling substrate for distributed heterogeneous information systems. München: R. Oldenbourg, 1991.

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Book chapters on the topic "Heterogeneous distributed systems"

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Wolff, T., and K. P. Löhr. "Transparently programming heterogeneous distributed systems." In Distributed Platforms, 399–412. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34947-3_30.

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Bhuvaneswari, M. C., and G. Subashini. "Scheduling in Heterogeneous Distributed Systems." In Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems, 147–69. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1958-3_9.

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Schwartz, David G. "Flipside: A Framework for Logic Programming Systems with Distributed Execution." In Cooperating Heterogeneous Systems, 61–80. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2211-9_5.

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Gu, Zhaoquan, Yuexuan Wang, Qiang-Sheng Hua, and Francis C. M. Lau. "Rendezvous in Heterogeneous Cognitive Radio Networks." In Rendezvous in Distributed Systems, 215–32. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3680-4_17.

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Cruz, Patricio J., Christoph Hintz, Jonathan West, and Rafael Fierro. "Optical Wireless Communications for Heterogeneous DARS." In Distributed Autonomous Robotic Systems, 219–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73008-0_16.

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Geihs, K., R. Staroste, and H. Eberle. "Operating System Support for Heterogeneous Distributed Systems." In Kommunikation in Verteilten Systemen, 178–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-71655-3_16.

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Kundu, Anirban, Chunlin Ji, and Ruopeng Liu. "Cloud Based Heterogeneous Distributed Framework." In Advances in Intelligent Systems and Computing, 471–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32063-7_50.

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Prorok, Amanda, and Vijay Kumar. "Towards Differentially Private Aggregation of Heterogeneous Robots." In Distributed Autonomous Robotic Systems, 587–601. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73008-0_41.

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Löhr, Klaus-Peter, Lutz Nentwig, and Joachim Müller. "DAPHNE support for distributed computing in heterogeneous environments." In Progress in Distributed Operating Systems and Distributed Systems Management, 138–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52609-9_81.

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Ngo, Hoang Giang, Luigi Liquori, and Chan Hung Nguyen. "Backward-Compatible Cooperation of Heterogeneous P2P Systems." In Distributed Computing and Networking, 287–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45249-9_19.

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Conference papers on the topic "Heterogeneous distributed systems"

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Kunzman, David M., and Laxmikant V. Kale. "Programming Heterogeneous Systems." In Distributed Processing, Workshops and Phd Forum (IPDPSW). IEEE, 2011. http://dx.doi.org/10.1109/ipdps.2011.377.

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"Transparently programming heterogeneous distributed systems." In 1996 IFIP/IEEE International Conference on Distributed Platforms. IEEE, 1996. http://dx.doi.org/10.1109/icdp.1996.864217.

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Zaschke, C., B. Essendorfer, and C. Kerth. "Interoperability of heterogeneous distributed systems." In SPIE Defense + Security, edited by Edward M. Carapezza. SPIE, 2016. http://dx.doi.org/10.1117/12.2223895.

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Meyer, Jan Christian, and Anne Cathrine Elster. "Performance modeling of heterogeneous systems." In Distributed Processing, Workshops and Phd Forum (IPDPSW). IEEE, 2010. http://dx.doi.org/10.1109/ipdpsw.2010.5470682.

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He, Yuxiong, Jie Liu, and Hongyang Sun. "Scheduling Functionally Heterogeneous Systems with Utilization Balancing." In Distributed Processing Symposium (IPDPS). IEEE, 2011. http://dx.doi.org/10.1109/ipdps.2011.113.

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Zhao Li, Tracey Ho, Derek Leong, and Hongyi Yao. "Distributed storage allocation for heterogeneous systems." In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013. http://dx.doi.org/10.1109/allerton.2013.6736541.

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Rantzer, Anders. "Distributed performance analysis of heterogeneous systems." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717216.

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Wang, Zhe, and Naftaly Minsky. "Fault Tolerance in Heterogeneous Distributed Systems." In 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. ICST, 2014. http://dx.doi.org/10.4108/icst.collaboratecom.2014.257585.

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de Micheli, Giovanni. "System-level design technologies for heterogeneous distributed systems." In the twenty-first annual symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1404371.1404378.

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Wilson, Lucas A. "Distributed, heterogeneous resource management using artificial immune systems." In Distributed Processing Symposium (IPDPS). IEEE, 2008. http://dx.doi.org/10.1109/ipdps.2008.4536363.

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Reports on the topic "Heterogeneous distributed systems"

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Stevens, B. W. Distributed Ada Programs on Heterogeneous Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada294848.

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Fujimoto, Richard, Michael Hunter, and Haesun Park. Dynamic Systems for Individual Tracking via Heterogeneous Information Integration and Crowd Source Distributed Simulation. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1004753.

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Navathe, Shamkant B. A Knowledge-Based Approach to Integrating and Querying Distributed Information Systems Heterogeneous Intelligent Processing for Engineering Design (HIPED). Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada341697.

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