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Статті в журналах з теми "Distributed Stream Processing Systems"
K, Sornalakshmi. "Dynamic Operator Scaling for Distributed Stream Processing Systems for Fluctuating Streams." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2815–21. http://dx.doi.org/10.5373/jardcs/v12sp7/20202422.
Повний текст джерелаWei, Xiaohui, Yuan Zhuang, Hongliang Li, and Zhiliang Liu. "Reliable stream data processing for elastic distributed stream processing systems." Cluster Computing 23, no. 2 (May 21, 2019): 555–74. http://dx.doi.org/10.1007/s10586-019-02939-9.
Повний текст джерелаShuiying Yu, Shuiying Yu, Yinting Zheng Shuiying Yu, Fan Zhang Yinting Zheng, Hanhua Chen Fan Zhang, and Hai Jin Hanhua Chen. "TriJoin: A Time-Efficient and Scalable Three-Way Distributed Stream Join System." 網際網路技術學刊 24, no. 2 (March 2023): 475–85. http://dx.doi.org/10.53106/160792642023032402024.
Повний текст джерелаShukla, Anshu, and Yogesh Simmhan. "Model-driven scheduling for distributed stream processing systems." Journal of Parallel and Distributed Computing 117 (July 2018): 98–114. http://dx.doi.org/10.1016/j.jpdc.2018.02.003.
Повний текст джерелаBernardelli de Moraes, Matheus, and André Leon Sampaio Gradvohl. "Evaluating the impact of a coordinated checkpointing in distributed data streams processing systems using discrete event simulation." Revista Brasileira de Computação Aplicada 12, no. 2 (May 19, 2020): 16–27. http://dx.doi.org/10.5335/rbca.v12i2.10295.
Повний текст джерелаTran, Tri Minh, and Byung Suk Lee. "Distributed stream join query processing with semijoins." Distributed and Parallel Databases 27, no. 3 (March 6, 2010): 211–54. http://dx.doi.org/10.1007/s10619-010-7062-7.
Повний текст джерелаHildrum, Kirsten, Fred Douglis, Joel L. Wolf, Philip S. Yu, Lisa Fleischer, and Akshay Katta. "Storage optimization for large-scale distributed stream-processing systems." ACM Transactions on Storage 3, no. 4 (February 2008): 1–28. http://dx.doi.org/10.1145/1326542.1326547.
Повний текст джерелаEskandari, Leila, Jason Mair, Zhiyi Huang, and David Eyers. "I-Scheduler: Iterative scheduling for distributed stream processing systems." Future Generation Computer Systems 117 (April 2021): 219–33. http://dx.doi.org/10.1016/j.future.2020.11.011.
Повний текст джерелаLiu, Xunyun, and Rajkumar Buyya. "Resource Management and Scheduling in Distributed Stream Processing Systems." ACM Computing Surveys 53, no. 3 (July 5, 2020): 1–41. http://dx.doi.org/10.1145/3355399.
Повний текст джерелаShukla, Anshu, Shilpa Chaturvedi, and Yogesh Simmhan. "RIoTBench: An IoT benchmark for distributed stream processing systems." Concurrency and Computation: Practice and Experience 29, no. 21 (October 4, 2017): e4257. http://dx.doi.org/10.1002/cpe.4257.
Повний текст джерелаДисертації з теми "Distributed Stream Processing Systems"
Vijayakumar, Nithya Nirmal. "Data management in distributed stream processing systems." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278228.
Повний текст джерелаSource: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6093. Adviser: Beth Plale. Title from dissertation home page (viewed May 9, 2008).
Drougas, Ioannis. "Rate allocation in distributed stream processing systems." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663077971&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268240766&clientId=48051.
Повний текст джерелаIncludes abstract. Title from first page of PDF file (viewed March 10, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 93-98). Also issued in print.
Bordin, Maycon Viana. "A benchmark suite for distributed stream processing systems." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163441.
Повний текст джерелаRecently a new application domain characterized by the continuous and low-latency processing of large volumes of data has been gaining attention. The growing number of applications of such genre has led to the creation of Stream Processing Systems (SPSs), systems that abstract the details of real-time applications from the developer. More recently, the ever increasing volumes of data to be processed gave rise to distributed SPSs. Currently there are in the market several distributed SPSs, however the existing benchmarks designed for the evaluation this kind of system covers only a few applications and workloads, while these systems have a much wider set of applications. In this work a benchmark for stream processing systems is proposed. Based on a survey of several papers with real-time and stream applications, the most used applications and areas were outlined, as well as the most used metrics in the performance evaluation of such applications. With these information the metrics of the benchmark were selected as well as a list of possible application to be part of the benchmark. Those passed through a workload characterization in order to select a diverse set of applications. To ease the evaluation of SPSs a framework was created with an API to generalize the application development and collect metrics, with the possibility of extending it to support other platforms in the future. To prove the usefulness of the benchmark, a subset of the applications were executed on Storm and Spark using the Azure Platform and the results have demonstrated the usefulness of the benchmark suite in comparing these systems.
Kakkad, Vasvi. "Curracurrong: a stream processing system for distributed environments." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12861.
Повний текст джерелаAl-Sinayyid, Ali. "JOB SCHEDULING FOR STREAMING APPLICATIONS IN HETEROGENEOUS DISTRIBUTED PROCESSING SYSTEMS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1868.
Повний текст джерелаBalazinska, Magdalena. "Fault-tolerance and load management in a distributed stream processing system." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/35287.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 187-199).
Advances in monitoring technology (e.g., sensors) and an increased demand for online information processing have given rise to a new class of applications that require continuous, low-latency processing of large-volume data streams. These "stream processing applications" arise in many areas such as sensor-based environment monitoring, financial services, network monitoring, and military applications. Because traditional database management systems are ill-suited for high-volume, low-latency stream processing, new systems, called stream processing engines (SPEs), have been developed. Furthermore, because stream processing applications are inherently distributed, and because distribution can improve performance and scalability, researchers have also proposed and developed distributed SPEs. In this dissertation, we address two challenges faced by a distributed SPE: (1) faulttolerant operation in the face of node failures, network failures, and network partitions, and (2) federated load management. For fault-tolerance, we present a replication-based scheme, called Delay, Process, and Correct (DPC), that masks most node and network failures.
(cont.) When network partitions occur, DPC addresses the traditional availability-consistency trade-off by maintaining, when possible, a desired availability specified by the application or user, but eventually also delivering the correct results. While maintaining the desired availability bounds, DPC also strives to minimize the number of inaccurate results that must later be corrected. In contrast to previous proposals for fault tolerance in SPEs, DPC simultaneously supports a variety of applications that differ in their preferred trade-off between availability and consistency. For load management, we present a Bounded-Price Mechanism (BPM) that enables autonomous participants to collaboratively handle their load without individually owning the resources necessary for peak operation. BPM is based on contracts that participants negotiate offline. At runtime, participants move load only to partners with whom they have a contract and pay each other the contracted price. We show that BPM provides incentives that foster participation and leads to good system-wide load distribution. In contrast to earlier proposals based on computational economies, BPM is lightweight, enables participants to develop and exploit preferential relationships, and provides stability and predictability.
(cont.) Although motivated by stream processing, BPM is general and can be applied to any federated system. We have implemented both schemes in the Borealis distributed stream processing engine. They will be available with the next release of the system.
by Magdalena Balazinska.
Ph.D.
Bustamante, Fabián Ernesto. "The active streams approach to adaptive distributed applications and services." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/15481.
Повний текст джерелаPenczek, Frank. "Static guarantees for coordinated components : a statically typed composition model for stream-processing networks." Thesis, University of Hertfordshire, 2012. http://hdl.handle.net/2299/9046.
Повний текст джерелаChen, Liang. "A grid-based middleware for processing distributed data streams." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1157990530.
Повний текст джерелаSree, Kumar Sruthi. "External Streaming State Abstractions and Benchmarking." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291338.
Повний текст джерелаDistribuerad dataströmsbehandling är ett populärt forskningsområde och är ett av de lovande paradigmen för snabbare och effektivare datahantering. Applicationstate är en förstklassig medborgare i nästan alla strömbehandlingssystem. Numera är strömbearbetning per definition statlig. För en strömbehandlingsapplikation backar staten operationer som aggregeringar, sammanfogningar och windows. Apache Flink är ett av de mest accepterade och mest använda strömbehandlingssystemen i branschen. En av de främsta anledningarna till att ingenjörer väljer ApacheFlink för att skriva och distribuera kontinuerliga applikationer är dess unika kombination av flexibilitet och skalbarhet för statlig programmerbarhet, och företaget garanterar att systemet säkerställer. Apache Flinks garantier gör alltid dess tillstånd korrekt och konsekvent även när noder misslyckas eller när antalet uppgifter ändras. Flink-tillstånd kan skala upp till dess beräkningsnods hårddiskgränser genom att använda inbäddade databaser för att lagra och hämta data. I allmänna tillståndsstöd som officiellt stöds av Flink är staten dock alltid tillgänglig lokalt för att beräkna uppgifter. Även om detta gör installationen bekvämare, skapar det andra utmaningar som icke-trivial tillståndskonfiguration och felåterställning. Samtidigt måste beräkning och tillstånd vara tätt kopplade. Den här strategin leder också till överanvändning och är kontraintuitiv för statligt intensiva endast arbetsbelastningar eller beräkningsintensiva endast arbetsbelastningar. Denna avhandling undersöker en alternativ statsbackendarkitektur, FlinkNDB, som kan hantera dessa utmaningar. FlinkNDB frikopplar tillstånd och beräknar med hjälp av en distribuerad databas för att lagra tillståndet. Avhandlingen täcker utmaningarna med befintliga statliga backends och designval och den nya implementeringen av statebackend. Vi har utvärderat genomförandet av FlinkNDBagainst befintliga statliga backends som erbjuds av Apache Flink.
Книги з теми "Distributed Stream Processing Systems"
J, Mullender Sape, ed. Distributed systems. New York, N.Y: ACM Press, 1989.
Знайти повний текст джерелаW, Chu Wesley, ed. Distributed systems. Dedham, MA: Artech House, 1986.
Знайти повний текст джерелаLangsford, Alwyn. Distributed systems management. Wokingham, Eng: Addison-Wesley, 1993.
Знайти повний текст джерелаCrowcroft, Jon. Open distributed systems. London: UCL Press, 1995.
Знайти повний текст джерелаOpen distributed systems. Boston: Artech House, 1995.
Знайти повний текст джерелаBal, H. E. Programming distributed systems. Summit, NJ, USA: Silicon Press, 1990.
Знайти повний текст джерелаT, Brazier F. M., Johansen D, and Institute of Electrical and Electronics Engineers., eds. Distributed open systems. Los Alamitos, Calif: IEEE Computer Society Press, 1994.
Знайти повний текст джерелаEngineering, University of Sheffield Department of Automatic Control and Systems. Parallel processing & distributed systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1992.
Знайти повний текст джерелаDistributed systems integration. Rijswijk, the Netherlands: Cap Gemini, 1991.
Знайти повний текст джерелаKhalil, Drira, Martelli Andrea, and Villemur Thierry, eds. Cooperative environments for distributed systems engineering: The distributed systems environment report. Berlin: Springer, 2001.
Знайти повний текст джерелаЧастини книг з теми "Distributed Stream Processing Systems"
Eibel, Christopher, Christian Gulden, Wolfgang Schröder-Preikschat, and Tobias Distler. "Strome: Energy-Aware Data-Stream Processing." In Distributed Applications and Interoperable Systems, 40–57. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93767-0_4.
Повний текст джерелаXia, Cathy H., James A. Broberg, Zhen Liu, and Li Zhang. "Distributed Resource Allocation in Stream Processing Systems." In Lecture Notes in Computer Science, 489–504. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11864219_34.
Повний текст джерелаKuralenok, Igor E., Artem Trofimov, Nikita Marshalkin, and Boris Novikov. "Deterministic Model for Distributed Speculative Stream Processing." In Advances in Databases and Information Systems, 233–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98398-1_16.
Повний текст джерелаCai, Rijun, Weigang Wu, Ning Huang, and Lihui Wu. "Processing Partially Ordered Requests in Distributed Stream Processing Systems." In Algorithms and Architectures for Parallel Processing, 211–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49583-5_16.
Повний текст джерелаZacheilas, Nikos, and Vana Kalogeraki. "DIsCO: DynamIc Data COmpression in Distributed Stream Processing Systems." In Distributed Applications and Interoperable Systems, 19–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59665-5_2.
Повний текст джерелаBattulga, Davaadorj, Daniele Miorandi, and Cédric Tedeschi. "SpecK: Composition of Stream Processing Applications over Fog Environments." In Distributed Applications and Interoperable Systems, 38–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78198-9_3.
Повний текст джерелаChen, Fei, Song Wu, and Hai Jin. "Network-Aware Grouping in Distributed Stream Processing Systems." In Algorithms and Architectures for Parallel Processing, 3–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05051-1_1.
Повний текст джерелаWang, Xiaotong, Cheng Jiang, Junhua Fang, Ke Shu, Rong Zhang, Weining Qian, and Aoying Zhou. "Evaluating Fault Tolerance of Distributed Stream Processing Systems." In Web and Big Data, 101–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60290-1_8.
Повний текст джерелаGorawski, Marcin, Pawel Marks, and Michal Gorawski. "Modeling Data Stream Intensity in Distributed Stream Processing System." In Computer Networks, 372–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_38.
Повний текст джерелаSegarra, Carlos, Ricard Delgado-Gonzalo, Mathieu Lemay, Pierre-Louis Aublin, Peter Pietzuch, and Valerio Schiavoni. "Using Trusted Execution Environments for Secure Stream Processing of Medical Data." In Distributed Applications and Interoperable Systems, 91–107. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22496-7_6.
Повний текст джерелаТези доповідей конференцій з теми "Distributed Stream Processing Systems"
Drougas, Yannis, and Vana Kalogeraki. "Accommodating bursts in distributed stream processing systems." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161015.
Повний текст джерелаKarimov, Jeyhun, Tilmann Rabl, Asterios Katsifodimos, Roman Samarev, Henri Heiskanen, and Volker Markl. "Benchmarking Distributed Stream Data Processing Systems." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00169.
Повний текст джерелаZvara, Zoltan, Peter G. N. Szabo, Gabor Hermann, and Andras Benczur. "Tracing Distributed Data Stream Processing Systems." In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). IEEE, 2017. http://dx.doi.org/10.1109/fas-w.2017.153.
Повний текст джерелаPacaci, Anil, and M. Tamer Özsu. "Distribution-Aware Stream Partitioning for Distributed Stream Processing Systems." In SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3206333.3206338.
Повний текст джерелаAffetti, Lorenzo. "Consistent Stream Processing." In DEBS '17: The 11th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3093742.3093900.
Повний текст джерелаEskandari, Leila, Jason Mair, Zhiyi Huang, and David Eyers. "Iterative Scheduling for Distributed Stream Processing Systems." In DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3210284.3219768.
Повний текст джерелаXie, Xing, Indrakshi Ray, Waruna Ranasinghe, Philips A. Gilbert, Pramod Shashidhara, and Anoop Yadav. "Distributed Multilevel Secure Data Stream Processing." In 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2013. http://dx.doi.org/10.1109/icdcsw.2013.64.
Повний текст джерелаLi, Kejian, Gang Liu, and Minhua Lu. "A Holistic Stream Partitioning Algorithm for Distributed Stream Processing Systems." In 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2019. http://dx.doi.org/10.1109/pdcat46702.2019.00046.
Повний текст джерелаYongluan Zhou, Karl Aberer, Ali Salehi, and Kian-Lee Tan. "Rethinking the design of distributed stream processing systems." In 2008 IEEE 24th International Conference on Data Engineeing workshop (ICDE Workshop 2008). IEEE, 2008. http://dx.doi.org/10.1109/icdew.2008.4498314.
Повний текст джерелаTuraga, Deepak S., Hyunggon Park, Rong Yan, and Olivier Verscheure. "Adaptive Multimedia Mining on Distributed Stream Processing Systems." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.159.
Повний текст джерелаЗвіти організацій з теми "Distributed Stream Processing Systems"
Popek, Gerald J., and Wesley W. Chu. Very Large Scale Distributed Information Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada243983.
Повний текст джерелаCho, Kilseok, Alan D. George, Raj Subramaniyan, and Keonwook Kim. Parallel Algorithms for Adaptive Matched-Field Processing in Distributed Array Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada465545.
Повний текст джерелаCho, Kilseok, Alan D. George, and Raj Subramaniyan. Fault-Tolerant Parallel Algorithms for Adaptive Matched-Field Processing on Distributed Array Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada466282.
Повний текст джерелаSmith, Bradley W. Distributed Computing for Signal Processing: Modeling of Asynchronous Parallel Computation. Appendix G. On the Design and Modeling of Special Purpose Parallel Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada167622.
Повний текст джерелаSchmitt, Harry. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444037.
Повний текст джерелаSchmitt, Harry A. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada454039.
Повний текст джерелаNavathe, 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.
Повний текст джерелаSchmitt, Harry A. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Netowrks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada464278.
Повний текст джерелаSchmitt, Harry A. Integrated Sensing and Processing (ISP) Phase 2: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada468089.
Повний текст джерелаChristopher, David A., and Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, May 2004. http://dx.doi.org/10.32747/2004.7586534.bard.
Повний текст джерела