Dynamic Replication Policy on HDFS Based on Machine Learning Clustering

Autor: Motaz A. Ahmed, Mohamed H. Khafagy, Masoud E. Shaheen, Mostafa R. Kaseb

Publikováno v: IEEE Access, Vol 11, Pp 18551-18559 (2023)

Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS s

Externí odkaz: https://doaj.org/article/961a1ff489be4c2784250925823a0329

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Evaluation Framework for Task Scheduling Algorithms in Distributed Reconfigurable Systems

In this dissertation, we propose the design of a simulation framework to investigate the performance of reconfigurable processors in distributed systems. The framework incorporates the partial reconfigurable functionality of the reconfigurable nodes.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dris___00893::315b57580417f5e4c29b9c3f197107e5
http://resolver.tudelft.nl/uuid:c9afa796-39a8-4487-8e56-2a7f020cedfe

Distributed Management of Massive Data: an Efficient Fine-Grain Data Access Scheme

Autor: Bogdan Nicolae, Luc Bougé, Gabriel Antoniu

Publikováno v: VECPAR '08: Proceedings of the 8th International Conference on High Performance Computing for Computational Science
VECPAR '08: Proceedings of the 8th International Conference on High Performance Computing for Computational Science, Jun 2008, Toulouse, France. pp.532-543, ⟨10.1007/978-3-540-92859-1_47⟩
High Performance Computing for Computational Science-VECPAR 2008 ISBN: 9783540928584
VECPAR

This paper addresses the problem of efficiently storing and accessing massive data blocks in a large-scale distributed environment, while providing efficient fine-grain access to data subsets. This issue is crucial in the context of applications in t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b084daa25877bd34d0e8f97bb0580cf
https://inria.hal.science/inria-00323248/file/paper.pdf

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