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Статті в журналах з теми "MAPREDUCE ARCHITECTURE"
Jiang, Tao, Huaxi Gu, Kun Wang, Xiaoshan Yu, and Yunfeng Lu. "BHyberCube: A MapReduce aware heterogeneous architecture for data center." Computer Science and Information Systems 14, no. 3 (2017): 611–27. http://dx.doi.org/10.2298/csis170202019t.
Повний текст джерелаPark, Jong-Hyuk, Hwa-Young Jeong, Young-Sik Jeong, and Min Choi. "REST-MapReduce: An Integrated Interface but Differentiated Service." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/170723.
Повний текст джерелаZhang, Bin, Jia Jin Le, and Mei Wang. "Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce." Applied Mechanics and Materials 575 (June 2014): 820–24. http://dx.doi.org/10.4028/www.scientific.net/amm.575.820.
Повний текст джерелаMitra, Arnab, Anirban Kundu, Matangini Chattopadhyay, and Samiran Chattopadhyay. "On the Exploration of Equal Length Cellular Automata Rules Targeting a MapReduce Design in Cloud." International Journal of Cloud Applications and Computing 8, no. 2 (April 2018): 1–26. http://dx.doi.org/10.4018/ijcac.2018040101.
Повний текст джерелаChen, Rong, and Haibo Chen. "Tiled-MapReduce." ACM Transactions on Architecture and Code Optimization 10, no. 1 (April 2013): 1–30. http://dx.doi.org/10.1145/2445572.2445575.
Повний текст джерелаLoughran, S., Jose M. Alcaraz Calero, A. Farrell, J. Kirschnick, and J. Guijarro. "Dynamic Cloud Deployment of a MapReduce Architecture." IEEE Internet Computing 16, no. 6 (November 2012): 40–50. http://dx.doi.org/10.1109/mic.2011.163.
Повний текст джерелаde Kruijf, M., and K. Sankaralingam. "MapReduce for the Cell Broadband Engine Architecture." IBM Journal of Research and Development 53, no. 5 (September 2009): 10:1–10:12. http://dx.doi.org/10.1147/jrd.2009.5429076.
Повний текст джерелаLiu, Hanpeng, Wuqi Gao, and Junmin Luo. "Research on Intelligentization of Cloud Computing Programs Based on Self-awareness." International Journal of Advanced Network, Monitoring and Controls 8, no. 2 (June 1, 2023): 89–98. http://dx.doi.org/10.2478/ijanmc-2023-0060.
Повний текст джерелаKhudhair, Muslim Mohsin, Adil AL-Rammahi, and Furkan Rabee. "An innovativefractal architecture model for implementing MapReduce in an open multiprocessing parallel environment." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 2 (May 1, 2023): 1059. http://dx.doi.org/10.11591/ijeecs.v30.i2.pp1059-1067.
Повний текст джерелаMarzuni, Saeed Mirpour, Abdorreza Savadi, Adel N. Toosi, and Mahmoud Naghibzadeh. "Cross-MapReduce: Data transfer reduction in geo-distributed MapReduce." Future Generation Computer Systems 115 (February 2021): 188–200. http://dx.doi.org/10.1016/j.future.2020.09.009.
Повний текст джерелаДисертації з теми "MAPREDUCE ARCHITECTURE"
Trezzo, Christopher J. "Continuous MapReduce an architecture for large-scale in-situ data processing /." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/fullcit?p1477939.
Повний текст джерелаTitle from first page of PDF file (viewed July 16, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (leaves 48-51).
Venumuddala, Ramu Reddy. "Distributed Frameworks Towards Building an Open Data Architecture." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801911/.
Повний текст джерелаKang, Seunghwa. "On the design of architecture-aware algorithms for emerging applications." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39503.
Повний текст джерелаFerreira, Leite Alessandro. "A user-centered and autonomic multi-cloud architecture for high performance computing applications." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112355/document.
Повний текст джерелаCloud computing has been seen as an option to execute high performance computing (HPC) applications. While traditional HPC platforms such as grid and supercomputers offer a stable environment in terms of failures, performance, and number of resources, cloud computing offers on-Demand resources generally with unpredictable performance at low financial cost. Furthermore, in cloud environment, failures are part of its normal operation. To overcome the limits of a single cloud, clouds can be combined, forming a cloud federation often with minimal additional costs for the users. A cloud federation can help both cloud providers and cloud users to achieve their goals such as to reduce the execution time, to achieve minimum cost, to increase availability, to reduce power consumption, among others. Hence, cloud federation can be an elegant solution to avoid over provisioning, thus reducing the operational costs in an average load situation, and removing resources that would otherwise remain idle and wasting power consumption, for instance. However, cloud federation increases the range of resources available for the users. As a result, cloud or system administration skills may be demanded from the users, as well as a considerable time to learn about the available options. In this context, some questions arise such as: (a) which cloud resource is appropriate for a given application? (b) how can the users execute their HPC applications with acceptable performance and financial costs, without needing to re-Engineer the applications to fit clouds' constraints? (c) how can non-Cloud specialists maximize the features of the clouds, without being tied to a cloud provider? and (d) how can the cloud providers use the federation to reduce power consumption of the clouds, while still being able to give service-Level agreement (SLA) guarantees to the users? Motivated by these questions, this thesis presents a SLA-Aware application consolidation solution for cloud federation. Using a multi-Agent system (MAS) to negotiate virtual machine (VM) migrations between the clouds, simulation results show that our approach could reduce up to 46% of the power consumption, while trying to meet performance requirements. Using the federation, we developed and evaluated an approach to execute a huge bioinformatics application at zero-Cost. Moreover, we could decrease the execution time in 22.55% over the best single cloud execution. In addition, this thesis presents a cloud architecture called Excalibur to auto-Scale cloud-Unaware application. Executing a genomics workflow, Excalibur could seamlessly scale the applications up to 11 virtual machines, reducing the execution time by 63% and the cost by 84% when compared to a user's configuration. Finally, this thesis presents a product line engineering (PLE) process to handle the variabilities of infrastructure-As-A-Service (IaaS) clouds, and an autonomic multi-Cloud architecture that uses this process to configure and to deal with failures autonomously. The PLE process uses extended feature model (EFM) with attributes to describe the resources and to select them based on users' objectives. Experiments realized with two different cloud providers show that using the proposed model, the users could execute their application in a cloud federation environment, without needing to know the variabilities and constraints of the clouds
Elteir, Marwa Khamis. "A MapReduce Framework for Heterogeneous Computing Architectures." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28786.
Повний текст джерелаPh. D.
Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.
Повний текст джерелаde, Souza Ferreira Tharso. "Improving Memory Hierarchy Performance on MapReduce Frameworks for Multi-Core Architectures." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/129468.
Повний текст джерелаThe need of analyzing large data sets from many different application fields has fostered the use of simplified programming models like MapReduce. Its current popularity is justified by being a useful abstraction to express data parallel processing and also by effectively hiding synchronization, fault tolerance and load balancing management details from the application developer. MapReduce frameworks have also been ported to multi-core and shared memory computer systems. These frameworks propose to dedicate a different computing CPU core for each map or reduce task to execute them concurrently. Also, Map and Reduce phases share a common data structure where main computations are applied. In this work we describe some limitations of current multi-core MapReduce frameworks. First, we describe the relevance of the data structure used to keep all input and intermediate data in memory. Current multi-core MapReduce frameworks are designed to keep all intermediate data in memory. When executing applications with large data input, the available memory becomes too small to store all framework intermediate data and there is a severe performance loss. We propose a memory management subsystem to allow intermediate data structures the processing of an unlimited amount of data by the use of a disk spilling mechanism. Also, we have implemented a way to manage concurrent access to disk of all threads participating in the computation. Finally, we have studied the effective use of the memory hierarchy by the data structures of the MapReduce frameworks and proposed a new implementation of partial MapReduce tasks to the input data set. The objective is to make a better use of the cache and to eliminate references to data blocks that are no longer in use. Our proposal was able to significantly reduce the main memory usage and improves the overall performance with the increasing of cache memory usage.
Adornes, Daniel Couto. "A unified mapreduce programming interface for multi-core and distributed architectures." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2015. http://tede2.pucrs.br/tede2/handle/tede/6782.
Повний текст джерелаMade available in DSpace on 2016-06-22T19:44:58Z (GMT). No. of bitstreams: 1 DIS_DANIEL_COUTO_ADORNES_COMPLETO.pdf: 1894086 bytes, checksum: f87c59fa92f43ed62efaafd9c724ed8d (MD5) Previous issue date: 2015-03-31
Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES
In order to improve performance, simplicity and scalability of large datasets processing, Google proposed the MapReduce parallel pattern. This pattern has been implemented in several ways for different architectural levels, achieving significant results for high performance computing. However, developing optimized code with those solutions requires specialized knowledge in each framework?s interface and programming language. Recently, the DSL-POPP was proposed as a framework with a high-level language for patternsoriented parallel programming, aimed at abstracting complexities of parallel and distributed code. Inspired on DSL-POPP, this work proposes the implementation of a unified MapReduce programming interface with rules for code transformation to optimized solutions for shared-memory multi-core and distributed architectures. The evaluation demonstrates that the proposed interface is able to avoid performance losses, while also achieving a code and a development cost reduction from 41.84% to 96.48%. Moreover, the construction of the code generator, the compatibility with other MapReduce solutions and the extension of DSL-POPP with the MapReduce pattern are proposed as future work.
Visando melhoria de performance, simplicidade e escalabilidade no processamento de dados amplos, o Google prop?s o padr?o paralelo MapReduce. Este padr?o tem sido implementado de variadas formas para diferentes n?veis de arquitetura, alcan?ando resultados significativos com respeito a computa??o de alto desempenho. No entanto, desenvolver c?digo otimizado com tais solu??es requer conhecimento especializado na interface e na linguagem de programa??o de cada solu??o. Recentemente, a DSL-POPP foi proposta como uma solu??o de linguagem de programa??o de alto n?vel para programa??o paralela orientada a padr?es, visando abstrair as complexidades envolvidas em programa??o paralela e distribu?da. Inspirado na DSL-POPP, este trabalho prop?e a implementa??o de uma interface unificada de programa??o MapReduce com regras para transforma??o de c?digo para solu??es otimizadas para arquiteturas multi-core de mem?ria compartilhada e distribu?da. A avalia??o demonstra que a interface proposta ? capaz de evitar perdas de performance, enquanto alcan?a uma redu??o de c?digo e esfor?o de programa??o de 41,84% a 96,48%. Ademais, a constru??o do gerador de c?digo, a compatibilidade com outras solu??es MapReduce e a extens?o da DSL-POPP com o padr?o MapReduce s?o propostas para trabalhos futuros.
Pan, Jie. "Modélisation et exécution des applications d'analyse de données multi-dimentionnelles sur architectures distribuées." Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00579125.
Повний текст джерелаPalanisamy, Balaji. "Cost-effective and privacy-conscious cloud service provisioning: architectures and algorithms." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52157.
Повний текст джерелаКниги з теми "MAPREDUCE ARCHITECTURE"
Herodotou, Herodotos, and Shivnath Babu. Massively Parallel Databases and MapReduce Systems. Now Publishers, 2013.
Знайти повний текст джерелаЧастини книг з теми "MAPREDUCE ARCHITECTURE"
Zhou, Lijun, and Zhiyi Yu. "Acceleration of MapReduce Framework on a Multicore Processor." In Emerging Technology and Architecture for Big-data Analytics, 175–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54840-1_8.
Повний текст джерелаXu, Hongsheng, Ganglong Fan, and Ke Li. "Analysis and Application of Mapreduce Architecture and Working Principle." In Advances in Intelligent Systems and Computing, 955–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15235-2_127.
Повний текст джерелаLaclavík, Michal, Martin Šeleng, and Ladislav Hluchý. "Towards Large Scale Semantic Annotation Built on MapReduce Architecture." In Computational Science – ICCS 2008, 331–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69389-5_38.
Повний текст джерелаEken, Süleyman, Umut Kizgindere, and Ahmet Sayar. "MapReduce Based Scalable Range Query Architecture for Big Spatial Data." In Lecture Notes in Geoinformation and Cartography, 263–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45123-7_19.
Повний текст джерелаTalan, Pooja P., Kartik U. Sharma, Pratiksha P. Nawade, and Karishma P. Talan. "An Overview of Hadoop MapReduce, Spark, and Scalable Graph Processing Architecture." In Advances in Intelligent Systems and Computing, 35–42. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1280-9_3.
Повний текст джерелаChen, Quan, and Minyi Guo. "MapReduce for Cloud Computing." In Task Scheduling for Multi-core and Parallel Architectures, 173–98. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6238-4_7.
Повний текст джерелаXu, Yujie, Wenyu Qu, Zhiyang Li, Changqing Ji, Yuanyuan Li, and Yinan Wu. "Fast Scalable k-means++ Algorithm with MapReduce." In Algorithms and Architectures for Parallel Processing, 15–28. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11194-0_2.
Повний текст джерелаHu, Minghao, Changjian Wang, Pengfei You, Zhen Huang, and Yuxing Peng. "Deadline-Oriented Task Scheduling for MapReduce Environments." In Algorithms and Architectures for Parallel Processing, 359–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_25.
Повний текст джерелаChen, Yi, Zhaobin Liu, Tingting Wang, and Lu Wang. "Load Balancing in MapReduce Based on Data Locality." In Algorithms and Architectures for Parallel Processing, 229–41. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11197-1_18.
Повний текст джерелаYu, Xiao, Jin Liu, Xiao Liu, Chuanxiang Ma, and Bin Li. "A MapReduce Reinforced Distributed Sequential Pattern Mining Algorithm." In Algorithms and Architectures for Parallel Processing, 183–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_13.
Повний текст джерелаТези доповідей конференцій з теми "MAPREDUCE ARCHITECTURE"
Ammal, R. Ananthalakshmi, and Kumar K. B. Aneesh. "MapReduce framework based distributed NMS architecture." In 2011 IFIP/IEEE International Symposium on Integrated Network Management (IM 2011). IEEE, 2011. http://dx.doi.org/10.1109/inm.2011.5990662.
Повний текст джерелаWang, Changjian, Yuxing Peng, Junyi Liu, Mingxing Tang, Guangming Liu, Jinghua Feng, and Pengfei You. "Optimal Task Scheduling in MapReduce." In 2014 9th IEEE International Conference on Networking, Architecture, and Storage (NAS). IEEE, 2014. http://dx.doi.org/10.1109/nas.2014.26.
Повний текст джерелаZhou, Xiaobo, and Bin Zhang. "Research of MapReduce architecture on busbar protection." In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691190.
Повний текст джерелаCaruana, Godwin, Maozhen Li, and Hao Qi. "SpamCloud: A MapReduce based anti-spam architecture." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569282.
Повний текст джерелаLiu, Lifeng, Yue Zhang, Meilin Liu, Chongjun Wang, and Jun Wang. "A-MapCG: An Adaptive MapReduce Framework for GPUs." In 2017 International Conference on Networking, Architecture, and Storage (NAS). IEEE, 2017. http://dx.doi.org/10.1109/nas.2017.8026842.
Повний текст джерелаWoldemariam, Yonas, Stefan Pletschacher, Christian Clausner, and Julian Bass. "A Cloud-Hosted MapReduce Architecture for Syntactic Parsing." In 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2019. http://dx.doi.org/10.1109/seaa.2019.00024.
Повний текст джерелаChen, Linchuan, Xin Huo, and Gagan Agrawal. "Accelerating MapReduce on a coupled CPU-GPU architecture." In 2012 SC - International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2012. http://dx.doi.org/10.1109/sc.2012.16.
Повний текст джерелаRanger, Colby, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, and Christos Kozyrakis. "Evaluating MapReduce for Multi-core and Multiprocessor Systems." In 2007 IEEE 13th International Symposium on High Performance Computer Architecture. IEEE, 2007. http://dx.doi.org/10.1109/hpca.2007.346181.
Повний текст джерелаFang Zhou, Hai Pham, Jianhui Yue, Hao Zou, and Weikuan Yu. "SFMapReduce: An optimized MapReduce framework for Small Files." In 2015 IEEE International Conference on Networking, Architecture and Storage (NAS). IEEE, 2015. http://dx.doi.org/10.1109/nas.2015.7255218.
Повний текст джерелаMa, Nam, Yinglong Xia, and Viktor K. Prasanna. "Parallel Exact Inference on Multicore Using MapReduce." In 2012 24th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2012. http://dx.doi.org/10.1109/sbac-pad.2012.43.
Повний текст джерела