Using software visualization to support the teaching of distributed programming
In this paper, we introduce MARVEL, a system designed to simplify the teaching of MapReduce, a popular distributed programming paradigm, through software visualization. At its core, it allows a teacher to describe and recreate a MapReduce application by interactively requesting, through a graphical...
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| Published in: | The Journal of supercomputing Vol. 79; no. 4; pp. 3974 - 3998 |
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| Format: | Journal Article |
| Language: | English |
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01.03.2023
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | In this paper, we introduce MARVEL, a system designed to simplify the teaching of MapReduce, a popular distributed programming paradigm, through software visualization. At its core, it allows a teacher to describe and recreate a MapReduce application by interactively requesting, through a graphical interface, the execution of a sequence of MapReduce transformations that target an input dataset. Then, the execution of each operation is illustrated on the screen by playing an appropriate graphical animation stage, highlighting aspects related to its distributed nature. The sequence of all animation stages, played back one after the other in a sequential order, results in a visualization of the whole algorithm. The content of the resulting visualization is not simulated or fictitious, but reflects the real behavior of the requested operations, thanks to the adoption of an architecture based on a real instance of a distributed system running on Apache Spark. On the teacher’s side, it is expected that by using MARVEL he/she will spend less time preparing materials and will be able to design a more interactive lesson than with electronic slides or a whiteboard. To test the effectiveness of the proposed approach on the learner side, we also conducted a small scientific experiment with a class of volunteer students who formed a control group. The results are encouraging, showing that the use of software visualization guarantees students a learning experience at least equivalent to that of conventional approaches. |
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| AbstractList | In this paper, we introduce MARVEL, a system designed to simplify the teaching of MapReduce, a popular distributed programming paradigm, through software visualization. At its core, it allows a teacher to describe and recreate a MapReduce application by interactively requesting, through a graphical interface, the execution of a sequence of MapReduce transformations that target an input dataset. Then, the execution of each operation is illustrated on the screen by playing an appropriate graphical animation stage, highlighting aspects related to its distributed nature. The sequence of all animation stages, played back one after the other in a sequential order, results in a visualization of the whole algorithm. The content of the resulting visualization is not simulated or fictitious, but reflects the real behavior of the requested operations, thanks to the adoption of an architecture based on a real instance of a distributed system running on Apache Spark. On the teacher’s side, it is expected that by using MARVEL he/she will spend less time preparing materials and will be able to design a more interactive lesson than with electronic slides or a whiteboard. To test the effectiveness of the proposed approach on the learner side, we also conducted a small scientific experiment with a class of volunteer students who formed a control group. The results are encouraging, showing that the use of software visualization guarantees students a learning experience at least equivalent to that of conventional approaches. |
| Author | Ferraro Petrillo, Umberto Palini, Francesco Di Rocco, Lorenzo |
| Author_xml | – sequence: 1 givenname: Lorenzo surname: Di Rocco fullname: Di Rocco, Lorenzo email: lorenzo.dirocco@uniroma1.it organization: Department of Statistical Sciences, Università di Roma “La Sapienza” – sequence: 2 givenname: Umberto surname: Ferraro Petrillo fullname: Ferraro Petrillo, Umberto organization: Department of Statistical Sciences, Università di Roma “La Sapienza” – sequence: 3 givenname: Francesco surname: Palini fullname: Palini, Francesco organization: Department of Statistical Sciences, Università di Roma “La Sapienza” |
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| Cites_doi | 10.1111/dsji.12197 10.7551/mitpress/5712.001.0001 10.1093/bioinformatics/bty018 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00088 10.1109/IPDPSW.2015.88 10.1016/j.jpdc.2018.02.019 10.1145/3287324.3287494 10.1016/j.jvlc.2007.05.001 10.1145/379088.1046618 10.1186/s12859-019-2694-8 10.1145/299649.299767 10.1145/2047594.2047641 |
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| Copyright | The Author(s) 2022 The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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ACM, New York, pp 165–170. https://doi.org/10.1145/2047594.2047641 VermaNMalhotraDSinghJBig data analytics for retail industry using mapreduce—a priori frameworkJ Manag Analyt202073424442 Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation (OSDI), vol 6, pp 137–150 Carr S, Fang C, Jozwowski T, Mayo J, Shene CK (2003) In: 2003 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), pp 1676–1682 EckrothJA course on big data analyticsJ Parallel Distrib Comput201811816617610.1016/j.jpdc.2018.02.019 Adams JC, Brown R, Matthews SJ, Shoop E (2021) In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, pp 342–349 Danner, A, Newhall, T, Webb KC (2019) In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 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IEEE, pp 0112–0118 Ngo LB, Duffy EB, Apon AW (2014) In: 2014 IEEE International on Parallel & Distributed Processing Symposium Workshops (IPDPSW). IEEE, pp 1114–1121 MatthewsSJUsing phoenix++ mapreduce to introduce undergraduate students to parallel computingJ Comput Sci Coll2017326165174 YangZGuoXTeaching Hadoop using role play gamesDecis Sci J Innov Educ202018162110.1111/dsji.12197 GeistABeguelinADongarraJJiangWManchekRSunderamVSPVM: parallel virtual machine: a users’ guide and tutorial for networked parallel computing1994MIT Press10.7551/mitpress/5712.001.00010849.68032 Brown ER, Garrity P, Yates T, Northfield M, Shoop E, Saint Paul M (2011) In: Midwest Instruction and Computing Symposium (Midwest Instruction and Computing Symposium), pp 758–758 Shamsi JA, Durrani NM, Kafi N (2015) In: 2015 IEEE International Parallel and Distributed Processing Symposium Workshop. 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| References_xml | – reference: Adams JC, Brown R, Matthews SJ, Shoop E (2021) In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, pp 342–349 – reference: Ben-AriMInteractive execution of distributed algorithmsJ Educ Resour Comput2001101145/384055 – reference: Danner, A, Newhall, T, Webb KC (2019) In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, pp 326–333 – reference: GeistABeguelinADongarraJJiangWManchekRSunderamVSPVM: parallel virtual machine: a users’ guide and tutorial for networked parallel computing1994MIT Press10.7551/mitpress/5712.001.00010849.68032 – reference: EckrothJA course on big data analyticsJ Parallel Distrib Comput201811816617610.1016/j.jpdc.2018.02.019 – reference: Ngo LB, Duffy EB, Apon AW (2014) In: 2014 IEEE International on Parallel & Distributed Processing Symposium Workshops (IPDPSW). IEEE, pp 1114–1121 – reference: StaskoJTTango: S framework and system for algorithm animationACM SIGCHI Bull1990213596010.1145/379088.1046618 – reference: Abdolazimi R, Heidari M, Esmaeilzadeh A, Naderi H (2022) In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp 0112–0118 – reference: Brown ER, Garrity P, Yates T, Northfield M, Shoop E, Saint Paul M (2011) In: Midwest Instruction and Computing Symposium (Midwest Instruction and Computing Symposium), pp 758–758 – reference: Naps TL, Chan EE (1999) In: The Proceedings of the Thirtieth SIGCSE Technical Symposium on Computer Science Education, SIGCSE ’99. ACM, New York, pp 232–236. https://doi.org/10.1145/299649.299767 – reference: Deb D, Fuad M, Irwin K (2019) In: Proceedings of the 50th ACM Technical Symposium on Computer Science Education, pp 2–8 – reference: Ferraro Petrillo U (2018) In: Au MH, Yiu SM, Li J, Luo X, Wang C, Castiglione A, Kluczniak K (eds) Network and system security. Springer, Cham, pp 349–360 – reference: Shamsi JA, Durrani NM, Kafi N (2015) In: 2015 IEEE International Parallel and Distributed Processing Symposium Workshop. IEEE, pp 772–778. https://doi.org/10.1109/IPDPSW.2015.88 – reference: MatthewsSJUsing phoenix++ mapreduce to introduce undergraduate students to parallel computingJ Comput Sci Coll2017326165174 – reference: Woods P (2012) The New Era of Big Data Security Analytics. https://searchsecurity.techtarget.com/feature/The-new-era-of-big-data-security-analytics – reference: Carr S, Fang C, Jozwowski T, Mayo J, Shene CK (2003) In: 2003 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), pp 1676–1682 – reference: Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, vol 10. USENIX Association, p 10 – reference: HoqueELeeHKillianCNita-RotaruCA testing platform for teaching secure distributed systems programming2016Purdue e-Pubs – reference: CattaneoGDe SantisAFerraro PetrilloUVisualization of cryptographic protocols with GRACEJ Vis Lang Comput200819225829010.1016/j.jvlc.2007.05.001 – reference: Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation (OSDI), vol 6, pp 137–150 – reference: HughesCHughesTParallel and distributed programming using C++2004Addison-Wesley Professional1061.57021 – reference: Ferraro-PetrilloURoscignoGCattaneoGGiancarloRInformational and linguistic analysis of large genomic sequence collections via efficient Hadoop cluster algorithmsBioinformatics (Oxford, England)201834111826183310.1093/bioinformatics/bty018 – reference: Bompiani E, Ferraro Petrillo U, Jona-Lasinio G, Palini F (2020) In: Proceedings of the 2020 IEEE International Conference on Dependable, Autonomic and Secure Computing. IEEE, pp 499–506. https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00088 – reference: Eckroth J (2016) In: Proceedings of the 47th ACM Technical Symposium on Computing Science Education. 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