Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters

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...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Kaitoua, Abdul Rahman (author)
مؤلفون آخرون: Hajj, Hazem (author), Saghir, Mazen A.R. (author), Artail, Hassan (author), Akkary, Haitham (author), Awad, Mariette (author), Sharafeddine, Mageda (author), Mershad, Khaleel (author)
التنسيق: article
منشور في: 2014
الوصول للمادة أونلاين:http://hdl.handle.net/10725/15388
https://doi.org/10.1145/2608199
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
https://dl.acm.org/doi/abs/10.1145/2608199
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author Kaitoua, Abdul Rahman
author2 Hajj, Hazem
Saghir, Mazen A.R.
Artail, Hassan
Akkary, Haitham
Awad, Mariette
Sharafeddine, Mageda
Mershad, Khaleel
author2_role author
author
author
author
author
author
author
author_facet Kaitoua, Abdul Rahman
Hajj, Hazem
Saghir, Mazen A.R.
Artail, Hassan
Akkary, Haitham
Awad, Mariette
Sharafeddine, Mageda
Mershad, Khaleel
author_role author
dc.creator.none.fl_str_mv Kaitoua, Abdul Rahman
Hajj, Hazem
Saghir, Mazen A.R.
Artail, Hassan
Akkary, Haitham
Awad, Mariette
Sharafeddine, Mageda
Mershad, Khaleel
dc.date.none.fl_str_mv 2014
2014-07-15
2024-03-07T11:25:59Z
2024-03-07T11:25:59Z
dc.identifier.none.fl_str_mv 1544-3566
http://hdl.handle.net/10725/15388
https://doi.org/10.1145/2608199
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.
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
https://dl.acm.org/doi/abs/10.1145/2608199
dc.language.none.fl_str_mv en
dc.relation.none.fl_str_mv ACM Transactions on Architecture and Code Optimization
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.title.none.fl_str_mv Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
dc.type.none.fl_str_mv Article
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description 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.
eu_rights_str_mv openAccess
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identifier_str_mv 1544-3566
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.
language_invalid_str_mv en
network_acronym_str LAURepo
network_name_str Lebanese American University repository
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spelling Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD ClustersKaitoua, Abdul RahmanHajj, HazemSaghir, Mazen A.R.Artail, HassanAkkary, HaithamAwad, MarietteSharafeddine, MagedaMershad, KhaleelIn 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.Published2024-03-07T11:25:59Z2024-03-07T11:25:59Z20142014-07-15Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1544-3566http://hdl.handle.net/10725/15388https://doi.org/10.1145/2608199Kaitoua, 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.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://dl.acm.org/doi/abs/10.1145/2608199enACM Transactions on Architecture and Code Optimizationinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/153882024-07-03T08:20:39Z
spellingShingle Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
Kaitoua, Abdul Rahman
status_str publishedVersion
title Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
title_full Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
title_fullStr Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
title_full_unstemmed Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
title_short Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
title_sort Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters
url http://hdl.handle.net/10725/15388
https://doi.org/10.1145/2608199
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
https://dl.acm.org/doi/abs/10.1145/2608199