Abstract
Volunteer computing is a type of distributed computing in which ordinary people donate processing and storage resources to scientific projects. BOINC is the main middleware system for this type of computing. The aim of volunteer computing is that organizations be able to attain large computing power thanks to the participation of volunteer clients instead of a high investment in infrastructure. There are projects, like the ATLAS@home project, in which the number of running jobs has reached a plateau, due to a high load on data servers caused by file transfer. This is why we have designed an alternative, using the same BOINC infrastructure, in order to improve the performance of BOINC projects that have reached their physical limit. This alternative involves having a percentage of the volunteer clients running as data servers, called data volunteers, that improve the performance by reducing the load on data servers. This paper describes our alternative in detail and shows the performance of the solution using a simulator of our own, ComBoS.
S. Alonso-Monsalve—This work has been partially funded by the grant TIN2013-41350-P of the Spanish Ministry of Economics and Competitiveness.
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Adam-Boundarios, C., Cameron, D., Filipcic, A., Lancon, E., Wu, W.: ATLAS@Home: harnessing volunteer computing for HEP. In: 21st International Conference on Computing in High Energy and Nuclear Physics, CHEP2015, Okinawa, Japan (2015)
Alonso-Monsalve, S., García-Carballeira, F., Calderón, A.: Analyzing the performance of volunteer computing for data intensive applications. In: 14th International Conference on High Performance Computing & Simulation, HPCS 2016, Innsbruck, Austria (2016)
Anderson, D.P.: BOINC: a system for public-resource computing and storage. In: 5th IEEE/ACM International Workshop on Grid Computing, pp. 4–10 (2004)
Anderson, D.P.: Local scheduling for volunteer computing. In: IEEE International Parallel and Distributed Processing Symposium, IPDPS 2007, pp. 1–8. IEEE (2007)
Anderson, D.P., Cobb, J., Korpela, E., Lebofsky, M., Werthimer, D.: SETI@home: an experiment in public-resource computing. Commun. ACM 45(11), 56–61 (2002)
Anderson, D., Korpela, E., Walton, R.: High-performance task distribution for volunteer computing. In: 2005 First International Conference on e-Science and Grid Computing, pp. 8–203 (2005)
ATLAS@home Project Status. http://atlasathome.cern.ch/server_status.php
Javadi, B., Kondo, D., Vincent, J.-M., Anderson, D.P.: Discovering statistical models of availability in large distributed systems: an empirical study of SETI@home. IEEE Trans. Parallel Distrib. Syst. 22, 1896–1903 (2011)
Balicki, J., Korłub, W., Paluszak, J.: Big data processing by volunteer computing supported by intelligent agents. In: Kryszkiewicz, M., Bandyopadhyay, S., Rybinski, H., Pal, S.K. (eds.) PReMI 2015. LNCS, vol. 9124, pp. 268–278. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19941-2_26
BOINC Jobs. https://boinc.berkeley.edu/trac/wiki/JobIn
BOINCstats. http://boincstats.com/en/stats
Bruno, R., Ferreira, P.: FreeCycles: efficient data distribution for volunteer computing. In: CloudDP 2014 Proceedings of the Fourth International Workshop on Cloud Data and Platforms (2014)
Casanova, H., Giersch, A., Legrand, A., Quinson, M., Suter, F.: Versatile, scalable, and accurate simulation of distributed applications and platforms. J. Parallel Distrib. Comput. 74(10), 2899–2917 (2014)
Climateprediction.net. http://climateprediction.net
Creating BOINC Projects. https://boinc.berkeley.edu/boinc.pdf
Donassolo, B., Casanova, H., Legrand, A., Velho, P.: Fast and scalable simulation of volunteer computing systems using SimGrid. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, pp. 605–612. ACM, New York (2010)
Einstein@home. http://www.einsteinathome.org
Elwaer, A., Taylor, I.J., Rana, O.: Optimizing data distribution in volunteer computing systems using resources of participants. Scalable Comput.: Pract. Exp. 12, 193–208 (2011)
García-López, P., Datta, A., Barcellos, M., Montresor, A., Higashino, T., Felber, P., Epema, D., Iamnitchi, A., Riviere, E.: Edge-centric computing: vision and challenges. ACM SIGCOMM Comput. Commun. 45, 37–42 (2015)
Kelly, I., Taulor, I.: Bridging the data management gap between service and desktop grids. In: Kacsuk, P., Lovas, R., Nemeth, Z. (eds.) Distributed and Parallel Systems In Focus: Desktop Grid Computing. Springer, Heidelberg (2008)
LIGO Scientific Collaboration, Anderson, D.P.: Einstein@Home search for periodic gravitational waves in early S5 LIGO data. Phys. Rev. D, 80, 042003 (2009)
Paul, P.: SETI@home project and its website. Crossroads 8(3), 3–5 (2002)
Top. 500 Supercomputer list. http://www.top500.org/
Volunteer Computing. http://boinc.berkeley.edu/trac/wiki/VolunteerComputing
Werthimer, D., Cobb, J., Lebofsky, M., Anderson, D., Korpela, E.: SETI@HOME–massively distributed computing for SETI. Comput. Sci. Eng. 3(1), 78–83 (2001)
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Alonso-Monsalve, S., García-Carballeira, F., Calderón, A. (2016). Improving the Performance of Volunteer Computing with Data Volunteers: A Case Study with the ATLAS@home Project. In: Carretero, J., Garcia-Blas, J., Ko, R., Mueller, P., Nakano, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10048. Springer, Cham. https://doi.org/10.1007/978-3-319-49583-5_13
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DOI: https://doi.org/10.1007/978-3-319-49583-5_13
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