Abstract
Distributed computing systems can be created using volunteers, users who spontaneously, after receiving an invitation, decide to provide their own resources or storage to contribute to a common effort. They can, for instance, run a script embedded in a web page; thus, collaboration is straightforward, but also ephemeral, with resources depending on the amount of time the user decides to lend. This implies that the user has to be kept engaged so as to obtain as many computing cycles as possible. In this paper, we analyze a volunteer-based evolutionary computing system called NodIO with the objective of discovering design decisions that encourage volunteer participation, thus increasing the overall computing power. We present the results of an experiment in which a gamification technique is applied by adding a leader-board showing the top scores achieved by registered contributors. In NodIO, volunteers can participate without creating an account, so one of the questions we wanted to address was if the need to register would have a negative impact on user participation. The experiment results show that even if only a small percentage of users created an account, those participating in the competition provided around 90% of the work, thus effectively increasing the performance of the overall system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Apolónia, N., Ferreira, P., Veiga, L.: Enhancing online communities with cycle-sharing for social networks. In: Abraham, A., Hassanien, A.E. (eds.) Computational Social Networks. Springer, London (2012). https://doi.org/10.1007/978-1-4471-4048-1_7
Boldrin, F., Taddia, C., Mazzini, G.: Distributed computing through web browser. In: 2007 IEEE 66th Vehicular Technology Conference (VTC-2007) Fall, pp. 2020–2024. IEEE (2007)
Cantú-Paz, E.: Migration policies, selection pressure, and parallel evolutionary algorithms. J. Heuristics 7(4), 311–334 (2001)
Deterding, S., Dixon, D., Khaled, R., Nacke, L.E.: From game design elements to gamefulness: defining gamification. In: Proceedings of MindTrek 2011, pp. 9–15. ACM, Tampere (2011). https://doi.org/10.1145/2181037.2181040
Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D.: Gamification: using game-design elements in non-gaming contexts. In: CHI’11 Extended Abstracts on Human Factors in Computing Systems, pp. 2425–2428. ACM (2011)
Dubois, D.J., Tamburrelli, G.: Understanding gamification mechanisms for software development. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, pp. 659–662. ACM, NY (2013)
Fabisiak, T., Danilecki, A.: Browser-based harnessing of voluntary computational power. Found. Comput. Decis. Sci. 42(1), 3–42 (2017)
Hamari, J., Koivisto, J., Sarsa, H.: Does gamification work? A literature review of empirical studies on gamification. In: 2014 47th Hawaii International Conference on System Sciences, pp. 3025–3034. IEEE (2014)
Hickman, T.: Total engagement: using games and virtual worlds to change the way people work and businesses (2010)
Huotari, K., Hamari, J.: Defining gamification: a service marketing perspective. In: Proceeding of the 16th International Academic MindTrek Conference, pp. 17–22. ACM (2012)
Klein, J., Spector, L.: Unwitting distributed genetic programming via asynchronous JavaScript and XML. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO 2007), pp. 1628–1635. ACM, New York (2007)
Langdon, W.B.: Pfeiffer - a distributed open-ended evolutionary system. In: Edmonds, B., Gilbert, N., Gustafson, S., Hales, D., Krasnogor, N. (eds.) AISB 2005: Proceedings of the Joint Symposium on Socially Inspired Computing (METAS 2005), pp. 7–13, sSAISB 2005 Convention. University of Hertfordshire, Hatfield (2005). http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_metas2005.pdf
Leclerc, G., Auerbach, J.E., Iacca, G., Floreano, D.: The seamless peer and cloud evolution framework. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 821–828. ACM (2016)
Merelo, J.J., García, A.M., Laredo, J.L.J., Lupión, J., Tricas, F.: Browser-based distributed evolutionary computation: performance and scaling behavior. In: GECCO 2007: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, pp. 2851–2858. ACM Press, New York (2007)
Merelo, J.J., García-Valdez, M., Castillo, P.A., García-Sánchez, P., de las Cuevas, P., Rico, N.: NodIO, a JavaScript framework for volunteer-based evolutionary algorithms: first results. Technical report, GeNeura/UGR/CITIC (2016). http://arxiv.org/abs/1601.01607
Merelo, J.J., Castillo, P.A., García-Sánchez, P., de las Cuevas, P., Rico, N., Valdez, M.G.: Performance for the masses: experiments with a web based architecture to harness volunteer resources for low cost distributed evolutionary computation. In: Friedrich, T., Neumann, F., Sutton, A.M. (eds.) Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, 20–24 July 2016, pp. 837–844. ACM, USA (2016). https://doi.org/10.1145/2908812.2908849
Merelo-Guervós, J.J., García-Sánchez, P.: Designing and modeling a browser-based distributed evolutionary computation system. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1117–1124. ACM (2015)
Milani, A.: Online genetic algorithms. Technical report, Institute of Information Theories and Applications FOI ITHEA (2004). http://hdl.handle.net/10525/838
Nijssen, S., Back, T.: An analysis of the behavior of simplified evolutionary algorithms on trap functions. IEEE Trans. Evol. Comput. 7(1), 11–22 (2003)
Pan, Y., White, J., Sun, Y., Gray, J.: Gray computing: an analysis of computing with background Javascript tasks. In: Proceedings of the 37th International Conference on Software Engineering, vol. 1, pp. 167–177. IEEE Press (2015)
Peñalver, J.G., Merelo, J.J.: Optimizing web page layout using an annealed genetic algorithm as client-side script. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 1018–1027. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056943
Quinn, A.J., Bederson, B.B.: Human computation: a survey and taxonomy of a growing field. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1403–1412. ACM, New York(2011)
Sarmenta, L.F.: Volunteer computing. Ph.D. thesis, Massachusetts Institute of Technology (2001)
Schmettow, M.: Sample size in usability studies. Commun. ACM 55(4), 64–70 (2012). https://doi.org/10.1145/2133806.2133824
Sherry, D., Veeramachaneni, K., McDermott, J., O’Reilly, U.-M.: Flex-GP: genetic programming on the cloud. In: Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 477–486. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29178-4_48
Vespignani, A., et al.: Predicting the behavior of techno-social systems. Science 325(5939), 425 (2009)
Acknowledgments
This work has been supported in part by: Ministerio español de Economía y Competitividad under projects TIN2014-56494-C4-3-P (UGR-EPHEMECH) and TIN2017-85727-C4-2-P (UGR-DeepBio).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
García-Valdez, M., Merelo Guervós, J.J., Lara, L., García-Sánchez, P. (2018). Increasing Performance via Gamification in a Volunteer-Based Evolutionary Computation System. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-91479-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91478-7
Online ISBN: 978-3-319-91479-4
eBook Packages: Computer ScienceComputer Science (R0)