Apache Hama: An Emerging Bulk Synchronous Parallel Computing Framework for Big Data Applications
In today's highly intertwined network society, the demand for big data processing frameworks is continuously growing. The widely adopted model to process big data is parallel and distributed computing. This paper documents the significant progress achieved in the field of distributed computing...
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| Veröffentlicht in: | IEEE access 2016, Vol.4, p.8879-8887 |
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| creator | Siddique, Kamran Akhtar, Zahid Yoon, Edward J. Jeong, Young-Sik Dasgupta, Dipankar Kim, Yangwoo |
| description | In today's highly intertwined network society, the demand for big data processing frameworks is continuously growing. The widely adopted model to process big data is parallel and distributed computing. This paper documents the significant progress achieved in the field of distributed computing frameworks, particularly Apache Hama, a top level project under the Apache Software Foundation, based on bulk synchronous parallel processing. The comparative studies and empirical evaluations performed in this paper reveal Hama's potential and efficacy in big data applications. In particular, we present a benchmark evaluation of Hama's graph package and Apache Giraph using PageRank algorithm. The results show that the performance of Hama is better than Giraph in terms of scalability and computational speed. However, despite great progress, a number of challenging issues continue to inhibit the full potential of Hama to be used at large scale. This paper also describes these challenges, analyzes solutions proposed to overcome them, and highlights research opportunities. |
| doi_str_mv | 10.1109/ACCESS.2016.2631549 |
| format | Article |
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The widely adopted model to process big data is parallel and distributed computing. This paper documents the significant progress achieved in the field of distributed computing frameworks, particularly Apache Hama, a top level project under the Apache Software Foundation, based on bulk synchronous parallel processing. The comparative studies and empirical evaluations performed in this paper reveal Hama's potential and efficacy in big data applications. In particular, we present a benchmark evaluation of Hama's graph package and Apache Giraph using PageRank algorithm. The results show that the performance of Hama is better than Giraph in terms of scalability and computational speed. However, despite great progress, a number of challenging issues continue to inhibit the full potential of Hama to be used at large scale. 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(IEEE) 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-542acdc02c150eb44753621b24fe76b4266dc9a6d55e218c6a445bfb725ee0963</citedby><cites>FETCH-LOGICAL-c408t-542acdc02c150eb44753621b24fe76b4266dc9a6d55e218c6a445bfb725ee0963</cites><orcidid>0000-0002-4038-3267 ; 0000-0002-7421-1105</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7752866$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27610,27900,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Siddique, Kamran</creatorcontrib><creatorcontrib>Akhtar, Zahid</creatorcontrib><creatorcontrib>Yoon, Edward J.</creatorcontrib><creatorcontrib>Jeong, Young-Sik</creatorcontrib><creatorcontrib>Dasgupta, Dipankar</creatorcontrib><creatorcontrib>Kim, Yangwoo</creatorcontrib><title>Apache Hama: An Emerging Bulk Synchronous Parallel Computing Framework for Big Data Applications</title><title>IEEE access</title><addtitle>Access</addtitle><description>In today's highly intertwined network society, the demand for big data processing frameworks is continuously growing. The widely adopted model to process big data is parallel and distributed computing. This paper documents the significant progress achieved in the field of distributed computing frameworks, particularly Apache Hama, a top level project under the Apache Software Foundation, based on bulk synchronous parallel processing. The comparative studies and empirical evaluations performed in this paper reveal Hama's potential and efficacy in big data applications. In particular, we present a benchmark evaluation of Hama's graph package and Apache Giraph using PageRank algorithm. The results show that the performance of Hama is better than Giraph in terms of scalability and computational speed. However, despite great progress, a number of challenging issues continue to inhibit the full potential of Hama to be used at large scale. 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| subjects | Algorithms Apache Hama Big Data BSP bulk synchronous parallel Comparative studies Computational modeling Computer architecture Computer networks Data processing distributed computing Distributed processing Empirical analysis Giraph Hadoop MapReduce Parallel processing Programming Search engines Servers Spark Synchronous systems |
| title | Apache Hama: An Emerging Bulk Synchronous Parallel Computing Framework for Big Data Applications |
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