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Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters

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dc.contributor.author Kaitoua, Abdul Rahman
dc.contributor.author Hajj, Hazem
dc.contributor.author Saghir, Mazen A.R.
dc.contributor.author Artail, Hassan
dc.contributor.author Akkary, Haitham
dc.contributor.author Awad, Mariette
dc.contributor.author Sharafeddine, Mageda
dc.contributor.author Mershad, Khaleel
dc.date.accessioned 2024-03-07T11:25:59Z
dc.date.available 2024-03-07T11:25:59Z
dc.date.copyright 2014 en_US
dc.date.issued 2014-07-15
dc.identifier.issn 1544-3566 en_US
dc.identifier.uri http://hdl.handle.net/10725/15388
dc.description.abstract In this article, we propose new extensions to Hadoop to enable clusters of reconfigurable active solid-state drives (RASSDs) to process streaming data from SSDs using FPGAs. We also develop an analytical model to estimate the performance of RASSD clusters running under Hadoop. Using the Hadoop RASSD platform and network simulators, we validate our design and demonstrate its impact on performance for different workloads taken from Stanford's Phoenix MapReduce project. Our results show that for a hardware acceleration factor of 20×, compute-intensive workloads processing 153MB of data can run up to 11× faster than a standard Hadoop cluster. en_US
dc.language.iso en en_US
dc.title Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SAS en_US
dc.author.idnumber 202203388 en_US
dc.author.department Computer Science And Mathematics en_US
dc.relation.journal ACM Transactions on Architecture and Code Optimization en_US
dc.journal.volume 11 en_US
dc.journal.issue 2 en_US
dc.article.pages 1-26 en_US
dc.identifier.doi https://doi.org/10.1145/2608199 en_US
dc.identifier.ctation Kaitoua, A., Hajj, H., Saghir, M. A., Artail, H., Akkary, H., Awad, M., ... & Mershad, K. (2014). Hadoop extensions for distributed computing on reconfigurable active SSD clusters. ACM Transactions on Architecture and Code Optimization (TACO), 11(2), 1-26. en_US
dc.author.email khaleel.mershad@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://dl.acm.org/doi/abs/10.1145/2608199 en_US
dc.orcid.id https://orcid.org/0000-0003-3786-5529 en_US
dc.author.affiliation Lebanese American University en_US


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