Real-Time Big Data Stream Processing Using GPU with Spark Over Hadoop Ecosystem

In this technological era, every person, authorities, entrepreneurs, businesses, and many things around us are connected to the internet, forming Internet of thing (IoT). This generates a massive amount of diverse data with very high-speed, termed as big data. However, this data is very useful that...

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Vydáno v:	International journal of parallel programming Ročník 46; číslo 3; s. 630 - 646
Hlavní autoři:	Rathore, M. Mazhar, Son, Hojae, Ahmad, Awais, Paul, Anand, Jeon, Gwanggil
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.06.2018 Springer Nature B.V
Témata:	Big Data Computer Science Data management Digital media Equivalence Graphics processing units Hand tools High speed Internet Internet of Things Parallel processing Processor Architectures Real time Software Engineering/Programming and Operating Systems Special Issue on Programming Models and Algorithms for Data Analysis in HPC Systems Theory of Computation Video data Big Data Spark GPU Hadoop MapReduce
ISSN:	0885-7458, 1573-7640
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Abstract	In this technological era, every person, authorities, entrepreneurs, businesses, and many things around us are connected to the internet, forming Internet of thing (IoT). This generates a massive amount of diverse data with very high-speed, termed as big data. However, this data is very useful that can be used as an asset for the businesses, organizations, and authorities to predict future in various aspects. However, efficiently processing Big Data while making real-time decisions is a quite challenging task. Some of the tools like Hadoop are used for Big Datasets processing. On the other hand, these tools could not perform well in the case of real-time high-speed stream processing. Therefore, in this paper, we proposed an efficient and real-time Big Data stream processing approach while mapping Hadoop MapReduce equivalent mechanism on graphics processing units (GPUs). We integrated a parallel and distributed environment of Hadoop ecosystem and a real-time streaming processing tool, i.e., Spark with GPU to make the system more powerful in order to handle the overwhelming amount of high-speed streaming. We designed a MapReduce equivalent algorithm for GPUs for a statistical parameter calculation by dividing overall Big Data files into fixed-size blocks. Finally, the system is evaluated while considering the efficiency aspect (processing time and throughput) using (1) large-size city traffic video data captured by static as well as moving vehicles’ cameras while identifying vehicles and (2) large text-based files, like twitter data files, structural data, etc. Results show that the proposed system working with Spark on top and GPUs under the parallel and distributed environment of Hadoop ecosystem is more efficient and real-time as compared to existing standalone CPU-based MapReduce implementation.
AbstractList	In this technological era, every person, authorities, entrepreneurs, businesses, and many things around us are connected to the internet, forming Internet of thing (IoT). This generates a massive amount of diverse data with very high-speed, termed as big data. However, this data is very useful that can be used as an asset for the businesses, organizations, and authorities to predict future in various aspects. However, efficiently processing Big Data while making real-time decisions is a quite challenging task. Some of the tools like Hadoop are used for Big Datasets processing. On the other hand, these tools could not perform well in the case of real-time high-speed stream processing. Therefore, in this paper, we proposed an efficient and real-time Big Data stream processing approach while mapping Hadoop MapReduce equivalent mechanism on graphics processing units (GPUs). We integrated a parallel and distributed environment of Hadoop ecosystem and a real-time streaming processing tool, i.e., Spark with GPU to make the system more powerful in order to handle the overwhelming amount of high-speed streaming. We designed a MapReduce equivalent algorithm for GPUs for a statistical parameter calculation by dividing overall Big Data files into fixed-size blocks. Finally, the system is evaluated while considering the efficiency aspect (processing time and throughput) using (1) large-size city traffic video data captured by static as well as moving vehicles’ cameras while identifying vehicles and (2) large text-based files, like twitter data files, structural data, etc. Results show that the proposed system working with Spark on top and GPUs under the parallel and distributed environment of Hadoop ecosystem is more efficient and real-time as compared to existing standalone CPU-based MapReduce implementation.
Author	Paul, Anand Jeon, Gwanggil Son, Hojae Ahmad, Awais Rathore, M. Mazhar
Author_xml	– sequence: 1 givenname: M. Mazhar surname: Rathore fullname: Rathore, M. Mazhar organization: School of Computer Science and Engineering, Kyungpook National University – sequence: 2 givenname: Hojae surname: Son fullname: Son, Hojae organization: School of Computer Science and Engineering, Kyungpook National University – sequence: 3 givenname: Awais surname: Ahmad fullname: Ahmad, Awais organization: Department of Information and Communication Engineering, Yeungnam University – sequence: 4 givenname: Anand surname: Paul fullname: Paul, Anand email: paul.editor@gmail.com organization: School of Computer Science and Engineering, Kyungpook National University – sequence: 5 givenname: Gwanggil surname: Jeon fullname: Jeon, Gwanggil organization: Department of Embedded Systems Engineering, Incheon National University
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Cites_doi	10.1137/1.9780898719604 10.1016/j.future.2015.08.004 10.1002/cpe.3403 10.1109/JIOT.2013.2296516 10.1109/JPROC.2008.917757 10.1016/j.jocs.2013.01.004 10.14778/1687553.1687576 10.1006/jpdc.2000.1714 10.1007/s10916-016-0647-6 10.1002/cpe.3504 10.1177/1094342014567907 10.1109/TITS.2013.2246835 10.1016/j.neucom.2015.04.109 10.1145/1327452.1327492 10.1109/TC.2011.112 10.1016/j.comnet.2015.12.023 10.1016/j.jpdc.2008.05.014 10.1145/1654059.1654079 10.1137/1.9780898719642 10.1109/SITIS.2015.121 10.1007/s11042-017-4393-7 10.1007/s10766-017-0498-x 10.1109/MICRO.2006.49 10.1109/IPDPS.2015.56 10.1109/HPCA.2007.346181 10.1145/1787275.1787328
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References	Rathore, Ahmad, Paul, Wan, Daqiang (CR8) 2016; 40 Rathore, Ahmad, Paul, Rho (CR9) 2016; 101 CR19 Sivaraman, Trivedi (CR5) 2013; 14 CR18 CR39 CR16 CR38 CR15 Dongarra (CR30) 2015; 2015 CR37 CR36 CR13 Che (CR22) 2008; 68 CR35 CR12 CR34 CR32 Ailamaki, Govindaraju, Harizopoulos, Manocha (CR14) 2006; 6 Cohen, Dolan, Dunlap, Hellerstein, Welton (CR1) 2009; 2 CR4 CR3 D’Amore (CR21) 2012; 7 CR6 Owens (CR23) 2008; 96 Ahmad, Paul, Rathore, Chang (CR7) 2016; 56 CR27 Braun (CR31) 2001; 61 Song, Dongarra (CR28) 2015; 27 CR24 Dean, Ghemawat (CR2) 2008; 51 Aldinucci (CR26) 2015; 29 Anderson, Bai, Bischof, Blackford, Demmel, Dongarra, Du Croz, Greenbaum, Hammarling, McKenney, Sorensen (CR33) 1999 CR20 Du (CR29) 2013; 4 CR42 CR41 CR40 Shi (CR25) 2012; 61 Cerotti (CR17) 2016; 28 Ahmad, Paul, Rathore (CR10) 2016; 174 Jin, Gubbi, Marusic, Palaniswami (CR11) 2014; 1 A Ahmad (513_CR7) 2016; 56 MM Rathore (513_CR8) 2016; 40 513_CR40 513_CR20 513_CR42 MM Rathore (513_CR9) 2016; 101 513_CR41 513_CR24 L D’Amore (513_CR21) 2012; 7 513_CR27 J Cohen (513_CR1) 2009; 2 P Du (513_CR29) 2013; 4 D Cerotti (513_CR17) 2016; 28 TD Braun (513_CR31) 2001; 61 J Dongarra (513_CR30) 2015; 2015 L Shi (513_CR25) 2012; 61 S Sivaraman (513_CR5) 2013; 14 A Ailamaki (513_CR14) 2006; 6 513_CR32 J Jin (513_CR11) 2014; 1 513_CR13 513_CR35 513_CR12 F Song (513_CR28) 2015; 27 513_CR34 513_CR15 513_CR37 A Ahmad (513_CR10) 2016; 174 513_CR36 513_CR39 513_CR16 513_CR38 513_CR19 513_CR18 E Anderson (513_CR33) 1999 M Aldinucci (513_CR26) 2015; 29 S Che (513_CR22) 2008; 68 J Dean (513_CR2) 2008; 51 JD Owens (513_CR23) 2008; 96 513_CR6 513_CR3 513_CR4
References_xml	– ident: CR18 – year: 1999 ident: CR33 publication-title: LAPACK Users’ Guide doi: 10.1137/1.9780898719604 – volume: 56 start-page: 493 year: 2016 end-page: 503 ident: CR7 article-title: Smart cyber society: integration of capillary devices with high usability based on cyber-physical system publication-title: Future Gen. Comput. Syst. doi: 10.1016/j.future.2015.08.004 – volume: 27 start-page: 3702 issue: 14 year: 2015 end-page: 3723 ident: CR28 article-title: A scalable approach to solving dense linear algebra problems on hybrid CPU-GPU systems publication-title: Concurr. Comput. Pract. Exp. doi: 10.1002/cpe.3403 – ident: CR4 – volume: 1 start-page: 112 issue: 2 year: 2014 end-page: 121 ident: CR11 article-title: An information framework for creating a smart city through internet of things publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2013.2296516 – ident: CR39 – ident: CR16 – volume: 96 start-page: 879 issue: 5 year: 2008 end-page: 899 ident: CR23 article-title: GPU computing publication-title: Proc. IEEE doi: 10.1109/JPROC.2008.917757 – ident: CR37 – ident: CR12 – volume: 4 start-page: 457 issue: 6 year: 2013 end-page: 464 ident: CR29 article-title: Soft error resilient QR factorization for hybrid system with GPGPU publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2013.01.004 – volume: 7 start-page: 91 issue: 3–4 year: 2012 end-page: 105 ident: CR21 article-title: HPC computation issues of the incremental 3D variational data assimilation scheme in OceanVar software publication-title: J. Numer. Anal. Ind. Appl. Math. – volume: 2 start-page: 1481 issue: 2 year: 2009 end-page: 1492 ident: CR1 article-title: Mad skills: new analysis practices for Big Data publication-title: Proc. VLDB Endow. doi: 10.14778/1687553.1687576 – ident: CR35 – ident: CR6 – volume: 61 start-page: 810 issue: 6 year: 2001 end-page: 837 ident: CR31 article-title: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems publication-title: J. Parallel Distrib. Comput. doi: 10.1006/jpdc.2000.1714 – volume: 40 start-page: 283 issue: 12 year: 2016 ident: CR8 article-title: Real-time medical emergency response system: exploiting IoT and Big Data for public health publication-title: J. Med. Syst. doi: 10.1007/s10916-016-0647-6 – ident: CR40 – ident: CR27 – ident: CR42 – volume: 28 start-page: 438 issue: 2 year: 2016 end-page: 452 ident: CR17 article-title: Modeling and analysis of performances for concurrent multithread applications on multicore and graphics processing unit systems publication-title: Concurr. Comput. Pract. Exp. doi: 10.1002/cpe.3504 – volume: 2015 start-page: 9 year: 2015 ident: CR30 article-title: Hpc programming on intel many-integrated-core hardware with magma port to xeon phi publication-title: Sci. Program. – ident: CR19 – volume: 29 start-page: 461 issue: 4 year: 2015 end-page: 472 ident: CR26 article-title: Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern publication-title: Int. J. High Perform. Comput. Appl. doi: 10.1177/1094342014567907 – volume: 6 start-page: 1267 year: 2006 end-page: 1267 ident: CR14 article-title: Query co-processing on commodity processors publication-title: VLDB – ident: CR3 – ident: CR15 – ident: CR38 – volume: 14 start-page: 906 issue: 2 year: 2013 end-page: 917 ident: CR5 article-title: Integrated lane and vehicle detection, localization, and tracking: a synergistic approach publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2013.2246835 – volume: 174 start-page: 439 year: 2016 end-page: 453 ident: CR10 article-title: An efficient divide-and-conquer approach for Big Data analytics in machine-to-machine communication publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.04.109 – ident: CR13 – volume: 51 start-page: 107 issue: 1 year: 2008 end-page: 113 ident: CR2 article-title: Mapreduce: simplified data processing on large clusters publication-title: Commun. ACM doi: 10.1145/1327452.1327492 – ident: CR32 – ident: CR34 – ident: CR36 – volume: 61 start-page: 804 issue: 6 year: 2012 end-page: 816 ident: CR25 article-title: vCUDA: GPU-accelerated high-performance computing in virtual machines publication-title: IEEE Trans. Comput. doi: 10.1109/TC.2011.112 – volume: 101 start-page: 63 year: 2016 end-page: 80 ident: CR9 article-title: Urban planning and building smart cities based on the internet of things using Big Data analytics publication-title: Comput. Netw. doi: 10.1016/j.comnet.2015.12.023 – ident: CR41 – volume: 68 start-page: 1370 issue: 10 year: 2008 end-page: 1380 ident: CR22 article-title: A performance study of general-purpose applications on graphics processors using CUDA publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2008.05.014 – ident: CR24 – ident: CR20 – volume: 68 start-page: 1370 issue: 10 year: 2008 ident: 513_CR22 publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2008.05.014 – volume: 61 start-page: 810 issue: 6 year: 2001 ident: 513_CR31 publication-title: J. Parallel Distrib. Comput. doi: 10.1006/jpdc.2000.1714 – volume: 40 start-page: 283 issue: 12 year: 2016 ident: 513_CR8 publication-title: J. Med. Syst. doi: 10.1007/s10916-016-0647-6 – ident: 513_CR36 doi: 10.1145/1654059.1654079 – ident: 513_CR13 – volume: 7 start-page: 91 issue: 3–4 year: 2012 ident: 513_CR21 publication-title: J. Numer. Anal. Ind. Appl. Math. – ident: 513_CR35 doi: 10.1137/1.9780898719642 – ident: 513_CR34 – ident: 513_CR6 doi: 10.1109/SITIS.2015.121 – ident: 513_CR15 – ident: 513_CR20 – ident: 513_CR3 – ident: 513_CR38 doi: 10.1007/s11042-017-4393-7 – volume: 4 start-page: 457 issue: 6 year: 2013 ident: 513_CR29 publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2013.01.004 – volume-title: LAPACK Users’ Guide year: 1999 ident: 513_CR33 doi: 10.1137/1.9780898719604 – ident: 513_CR37 doi: 10.1007/s10766-017-0498-x – volume: 14 start-page: 906 issue: 2 year: 2013 ident: 513_CR5 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2013.2246835 – volume: 27 start-page: 3702 issue: 14 year: 2015 ident: 513_CR28 publication-title: Concurr. Comput. Pract. Exp. doi: 10.1002/cpe.3403 – ident: 513_CR24 – ident: 513_CR41 – ident: 513_CR18 doi: 10.1109/MICRO.2006.49 – ident: 513_CR27 doi: 10.1109/IPDPS.2015.56 – volume: 29 start-page: 461 issue: 4 year: 2015 ident: 513_CR26 publication-title: Int. J. High Perform. Comput. Appl. doi: 10.1177/1094342014567907 – ident: 513_CR12 – ident: 513_CR39 – volume: 61 start-page: 804 issue: 6 year: 2012 ident: 513_CR25 publication-title: IEEE Trans. Comput. doi: 10.1109/TC.2011.112 – volume: 56 start-page: 493 year: 2016 ident: 513_CR7 publication-title: Future Gen. Comput. Syst. doi: 10.1016/j.future.2015.08.004 – volume: 96 start-page: 879 issue: 5 year: 2008 ident: 513_CR23 publication-title: Proc. IEEE doi: 10.1109/JPROC.2008.917757 – volume: 101 start-page: 63 year: 2016 ident: 513_CR9 publication-title: Comput. Netw. doi: 10.1016/j.comnet.2015.12.023 – volume: 174 start-page: 439 year: 2016 ident: 513_CR10 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.04.109 – volume: 1 start-page: 112 issue: 2 year: 2014 ident: 513_CR11 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2013.2296516 – volume: 51 start-page: 107 issue: 1 year: 2008 ident: 513_CR2 publication-title: Commun. ACM doi: 10.1145/1327452.1327492 – volume: 2 start-page: 1481 issue: 2 year: 2009 ident: 513_CR1 publication-title: Proc. VLDB Endow. doi: 10.14778/1687553.1687576 – ident: 513_CR4 – volume: 28 start-page: 438 issue: 2 year: 2016 ident: 513_CR17 publication-title: Concurr. Comput. Pract. Exp. doi: 10.1002/cpe.3504 – volume: 2015 start-page: 9 year: 2015 ident: 513_CR30 publication-title: Sci. Program. – ident: 513_CR16 doi: 10.1109/HPCA.2007.346181 – ident: 513_CR32 doi: 10.1137/1.9780898719604 – volume: 6 start-page: 1267 year: 2006 ident: 513_CR14 publication-title: VLDB – ident: 513_CR19 doi: 10.1145/1787275.1787328 – ident: 513_CR42 – ident: 513_CR40
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SubjectTerms	Big Data Computer Science Data management Digital media Equivalence Graphics processing units Hand tools High speed Internet Internet of Things Parallel processing Processor Architectures Real time Software Engineering/Programming and Operating Systems Special Issue on Programming Models and Algorithms for Data Analysis in HPC Systems Theory of Computation Video data
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Title	Real-Time Big Data Stream Processing Using GPU with Spark Over Hadoop Ecosystem
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