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Опубліковано: 6 вересня 2023

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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.

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Wei, 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.

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Shuiying 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.

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<p>Stream join is one of the most fundamental operations in data stream processing applications. Existing distributed stream join systems can support efficient two-way join, which is a join operation between two streams. Based the two-way join, implementing a three-way join require to be split into double two-way joins, where the second two-way join needs to wait for the join result transmitted from the first two-way join. We show through experiments that such a design raises prohibitively high processing latency. To solve this problem, we propose TriJoin, a time-efficient three-way distributed stream join system. We design a symmetric wait-free structure by symmetrically partitioning tuples and reused join. TriJoin utilizes reused join to join each new tuple with the intermediate result of the other two streams and stored tuples locally. For a new tuple, TriJoin only joins it with the intermediate result to generate the final result without waiting, greatly reducing the processing latency. In TriJoin, we design two partitioning and storage schemes according to two different forms of three-way stream join. We implement TriJoin and conduct comprehensive experiments to evaluate the performance using real-world traces. Results show that TriJoin significantly reduces the processing latency by up to 68%, compared to existing designs.</p> <p> </p>

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Shukla, 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.

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Bernardelli 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.

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Data Streams Processing systems process continuous flows of data under Quality of Service requirements. Data streams often contain critical information which requires real-time processing. To guarantee systems' dependability and avoid information loss, one must use a fault-tolerance strategy. However, there are several strategies available, and the proper evaluation of which mechanism is better for each system architecture is challenging, especially in large-scale distributed systems. In this paper, we propose a discrete simulation model for investigating the impacts of the Coordinated Checkpoint fault tolerance strategy imposes on Data Stream Processing Systems. Results show that this strategy critically affects stream processing in failure-prone situations due to an increase in latency up to 120% and information loss, reaching 95% of the processing window in the worst case.

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Tran, 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.

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Hildrum, 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.

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Eskandari, 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.

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Liu, 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.

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Shukla, 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.

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Дисертації 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.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2007.
Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6093. Adviser: Beth Plale. Title from dissertation home page (viewed May 9, 2008).

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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.

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Thesis (Ph. D.)--University of California, Riverside, 2008.
Includes 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.

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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.

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Um dado por si só não possui valor algum, a menos que ele seja interpretado, contextualizado e agregado com outros dados, para então possuir valor, tornando-o uma informação. Em algumas classes de aplicações o valor não está apenas na informação, mas também na velocidade com que essa informação é obtida. As negociações de alta frequência (NAF) são um bom exemplo onde a lucratividade é diretamente proporcional a latência (LOVELESS; STOIKOV; WAEBER, 2013). Com a evolução do hardware e de ferramentas de processamento de dados diversas aplicações que antes levavam horas para produzir resultados, hoje precisam produzir resultados em questão de minutos ou segundos (BARLOW, 2013). Este tipo de aplicação tem como característica, além da necessidade de processamento em tempo-real ou quase real, a ingestão contínua de grandes e ilimitadas quantidades de dados na forma de tuplas ou eventos. A crescente demanda por aplicações com esses requisitos levou a criação de sistemas que disponibilizam um modelo de programação que abstrai detalhes como escalonamento, tolerância a falhas, processamento e otimização de consultas. Estes sistemas são conhecidos como Stream Processing Systems (SPS), Data Stream Management Systems (DSMS) (CHAKRAVARTHY, 2009) ou Stream Processing Engines (SPE) (ABADI et al., 2005). Ultimamente estes sistemas adotaram uma arquitetura distribuída como forma de lidar com as quantidades cada vez maiores de dados (ZAHARIA et al., 2012). Entre estes sistemas estão S4, Storm, Spark Streaming, Flink Streaming e mais recentemente Samza e Apache Beam. Estes sistemas modelam o processamento de dados através de um grafo de fluxo com vértices representando os operadores e as arestas representando os data streams. Mas as similaridades não vão muito além disso, pois cada sistema possui suas particularidades com relação aos mecanismos de tolerância e recuperação a falhas, escalonamento e paralelismo de operadores, e padrões de comunicação. Neste senário seria útil possuir uma ferramenta para a comparação destes sistemas em diferentes workloads, para auxiliar na seleção da plataforma mais adequada para um trabalho específico. Este trabalho propõe um benchmark composto por aplicações de diferentes áreas, bem como um framework para o desenvolvimento e avaliação de SPSs distribuídos.
Recently 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.

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Kakkad, Vasvi. "Curracurrong: a stream processing system for distributed environments." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12861.

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Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay.

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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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Balazinska, 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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.
This 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.

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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.

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Penczek, 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.

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Does your program do what it is supposed to be doing? Without running the program providing an answer to this question is much harder if the language does not support static type checking. Of course, even if compile-time checks are in place only certain errors will be detected: compilers can only second-guess the programmer’s intention. But, type based techniques go a long way in assisting programmers to detect errors in their computations earlier on. The question if a program behaves correctly is even harder to answer if the program consists of several parts that execute concurrently and need to communicate with each other. Compilers of standard programming languages are typically unable to infer information about how the parts of a concurrent program interact with each other, especially where explicit threading or message passing techniques are used. Hence, correctness guarantees are often conspicuously absent. Concurrency management in an application is a complex problem. However, it is largely orthogonal to the actual computational functionality that a program realises. Because of this orthogonality, the problem can be considered in isolation. The largest possible separation between concurrency and functionality is achieved if a dedicated language is used for concurrency management, i.e. an additional program manages the concurrent execution and interaction of the computational tasks of the original program. Such an approach does not only help programmers to focus on the core functionality and on the exploitation of concurrency independently, it also allows for a specialised analysis mechanism geared towards concurrency-related properties. This dissertation shows how an approach that completely decouples coordination from computation is a very supportive substrate for inferring static guarantees of the correctness of concurrent programs. Programs are described as streaming networks connecting independent components that implement the computations of the program, where the network describes the dependencies and interactions between components. A coordination program only requires an abstract notion of computation inside the components and may therefore be used as a generic and reusable design pattern for coordination. A type-based inference and checking mechanism analyses such streaming networks and provides comprehensive guarantees of the consistency and behaviour of coordination programs. Concrete implementations of components are deliberately left out of the scope of coordination programs: Components may be implemented in an external language, for example C, to provide the desired computational functionality. Based on this separation, a concise semantic framework allows for step-wise interpretation of coordination programs without requiring concrete implementations of their components. The framework also provides clear guidance for the implementation of the language. One such implementation is presented and hands-on examples demonstrate how the language is used in practice.

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Chen, 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.

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Sree, 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.

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Distributed data stream processing is a popular research area and is one of the promising paradigms for faster and efficient data management. Application state is a first-class citizen in nearly every stream processing system. Nowadays, stream processing is, by definition, stateful. For a stream processing application, the state is backing operations such as aggregations, joins, and windows. Apache Flink is one of the most accepted and widely used stream processing systems in the industry. One of the main reasons engineers choose Apache Flink to write and deploy continuous applications is its unique combination of flexibility and scalability for stateful programmability, and the firm guarantee that the system ensures. Apache Flink’s guarantees always make its states correct and consistent even when nodes fail or when the number of tasks changes. Flink state can scale up to its compute node’s hard disk boundaries using embedded databases to store and retrieve data. Nevertheless, in all existing state backends officially supported by Flink, the state is always available locally to compute tasks. Even though this makes deployment more convenient, it creates other challenges such as non-trivial state reconfiguration and failure recovery. At the same time, compute, and state are bound to be tightly coupled. This strategy also leads to over-provisioning and is counterintuitive on state intensive only workloads or compute-intensive only workloads. This thesis investigates an alternative state backend architecture, FlinkNDB, which can tackle these challenges. FlinkNDB decouples state and computes by using a distributed database to store the state. The thesis covers the challenges of existing state backends and design choices and the new state backend implementation. We have evaluated the implementation of FlinkNDB against existing state backends offered by Apache Flink.
Distribuerad 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.

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Книги з теми "Distributed Stream Processing Systems"

J, Mullender Sape, ed. Distributed systems. New York, N.Y: ACM Press, 1989.

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W, Chu Wesley, ed. Distributed systems. Dedham, MA: Artech House, 1986.

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Langsford, Alwyn. Distributed systems management. Wokingham, Eng: Addison-Wesley, 1993.

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Crowcroft, Jon. Open distributed systems. London: UCL Press, 1995.

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Open distributed systems. Boston: Artech House, 1995.

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Bal, H. E. Programming distributed systems. Summit, NJ, USA: Silicon Press, 1990.

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T, Brazier F. M., Johansen D, and Institute of Electrical and Electronics Engineers., eds. Distributed open systems. Los Alamitos, Calif: IEEE Computer Society Press, 1994.

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Engineering, 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.

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Distributed systems integration. Rijswijk, the Netherlands: Cap Gemini, 1991.

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Khalil, Drira, Martelli Andrea, and Villemur Thierry, eds. Cooperative environments for distributed systems engineering: The distributed systems environment report. Berlin: Springer, 2001.

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Частини 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.

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Xia, 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.

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Kuralenok, 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.

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Cai, 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.

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Zacheilas, 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.

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Battulga, 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.

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Chen, 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.

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Wang, 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.

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Gorawski, 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.

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Segarra, 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.

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Тези 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.

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Karimov, 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.

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Zvara, 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.

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Pacaci, 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.

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Affetti, 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.

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Eskandari, 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.

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Xie, 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.

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Li, 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.

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Yongluan 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.

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Turaga, 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.

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Звіти 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.

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Cho, 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.

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Cho, 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.

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Smith, 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.

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Schmitt, 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.

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Schmitt, 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.

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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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Schmitt, 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.

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Schmitt, 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.

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Christopher, 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.

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Анотація:

Original Objectives: 1. Purify and biochemically characterize RB60 orthologs in higher plant chloroplasts; 2. Clone the gene(s) encoding plant RB60 orthologs and determine their structure and expression; 3. Manipulate the expression of RB60; 4. Assay the effects of altered RB60 expression on thylakoid biogenesis and photosynthetic function in plants exposed to different light conditions. In addition, we also examined the gene structure and expression of RB60 orthologs in the non-vascular plant, Physcomitrella patens and cloned the poly(A)-binding protein orthologue (43 kDa RB47-like protein). This protein is believed to a partner that interacts with RB60 to bind to the psbA5' UTR. Thus, to obtain a comprehensive view of RB60 function requires analysis of its biochemical partners such as RB43. Background & Achievements: High levels of sunlight reduce photosynthesis in plants by damaging the photo system II reaction center (PSII) subunits, such as D1 (encoded by the chloroplast tpsbAgene). When the rate of D1 synthesis is less than the rate of photo damage, photo inhibition occurs and plant growth is decreased. Plants use light-activated translation and enhanced psbAmRNA stability to maintain D1 synthesis and replace the photo damaged 01. Despite the importance to photosynthetic capacity, these mechanisms are poorly understood in plants. One intriguing model derived from the algal chloroplast system, Chlamydomonas, implicates the role of three proteins (RB60, RB47, RB38) that bind to the psbAmRNA 5' untranslated leader (5' UTR) in the light to activate translation or enhance mRNA stability. RB60 is the key enzyme, protein D1sulfide isomerase (Pill), that regulates the psbA-RN :Binding proteins (RB's) by way of light-mediated redox potentials generated by the photosystems. However, proteins with these functions have not been described from higher plants. We provided compelling evidence for the existence of RB60, RB47 and RB38 orthologs in the vascular plant, Arabidopsis. Using gel mobility shift, Rnase protection and UV-crosslinking assays, we have shown that a dithiol redox mechanism which resembles a Pill (RB60) activity regulates the interaction of 43- and 30-kDa proteins with a thermolabile stem-loop in the 5' UTR of the psbAmRNA from Arabidopsis. We discovered, in Arabidopsis, the PD1 gene family consists of II members that differ in polypeptide length from 361 to 566 amino acids, presence of signal peptides, KDEL motifs, and the number and positions of thioredoxin domains. PD1's catalyze the reversible formation an disomerization of disulfide bonds necessary for the proper folding, assembly, activity, and secretion of numerous enzymes and structural proteins. PD1's have also evolved novel cellular redox functions, as single enzymes and as subunits of protein complexes in organelles. We provide evidence that at least one Pill is localized to the chloroplast. We have used PDI-specific polyclonal and monoclonal antisera to characterize the PD1 (55 kDa) in the chloroplast that is unevenly distributed between the stroma and pellet (containing membranes, DNA, polysomes, starch), being three-fold more abundant in the pellet phase. PD1-55 levels increase with light intensity and it assembles into a high molecular weight complex of ~230 kDa as determined on native blue gels. In vitro translation of all 11 different Pill's followed by microsomal membrane processing reactions were used to differentiate among PD1's localized in the endoplasmic reticulum or other organelles. These results will provide.1e insights into redox regulatory mechanisms involved in adaptation of the photosynthetic apparatus to light stress. Elucidating the genetic mechanisms and factors regulating chloroplast photosynthetic genes is important for developing strategies to improve photosynthetic efficiency, crop productivity and adaptation to high light environments.

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