Abstract
Many of the hundreds of millions Massively Multiplayer Online Games (MMOGs) players are also involved in the social networks built around the MMOGs they play. Through these networks, these players exchange game news, advice, and expertise, and expect in return support such as player reports and clan statistics. Thus, the MMOG social networks need to collect and analyze MMOG data, in a process of continuous MMOG analytics. In this chapter we investigate the use of CAMEO, an architecture for Continuous Analytics for Massively multiplayEr Online games on cloud resources, to support the analytics part of MMOG social networks. We present the design and implementation of CAMEO, with a focus on the cloud-related benefits and challenges. We also use CAMEO to do continuous analytics on a real MMOG community of over 5,000,000 players, thus performing the largest study of an online community, to-date.
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References
Alonso, R., Barbará, D., Garcia-Molina, H.: Data caching issues in an information retrieval system. ACM Trans. Database Syst. 15(3), 359–384 (1990)
Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A., Raghavan, S.: Searching the web. ACM Trans. Internet Technol. 1(1), 2–43 (2001), http://doi.acm.org/10.1145/383034.383035
Bayardo, R.J., Ma, Y., Srikant, R.: Scaling up all pairs similarity search. In: International Conference on the World Wide Web (WWW), pp. 131–140 (2007)
BBC NEws, Virtual game is a ’disease model’. News Item (2009), http://news.bbc.co.uk/2/hi/6951918.stm
Berman, F.: Got data?: a guide to data preservation in the information age. Commun. ACM 51(12), 50–56 (2008)
Boto: Boto - a python interface to amazon web services, http://code.google.com/p/boto/
Castronova, E.: On virtual economies. Game Studies 3(2) (2003)
Cho, J., Garcia-Molina, H.: Parallel crawlers. In: International Conference on the World Wide Web (WWW), pp. 124–135 (2002)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Foster, I.T., Kesselman, C., Tuecke, S.: The anatomy of the grid: Enabling scalable virtual organizations. Int’l. J. of High Performance Computing Applications 15(3), 200–222 (2001)
Fritsch, T., Voigt, B., Schiller, J.H.: Distribution of online hardcore player behavior (how hardcore are you?). In: Workshop on Network and System Support for Games (NETGAMES), p. 16 (2006)
Ghemawat, S., Gobioff, H., Leung, S.T.: The Google File System. SIGOPS Oper. Syst. Rev. 37(5), 29–43 (2003)
Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W., Goldberg-Kidon, J.: Google Fusion Tables: web-centered data management and collaboration. In: SIGMOD 2010: Proceedings of the 2010 International Conference on Management of Data, pp. 1061–1066. ACM, New York (2010)
Grossman, R.L., Gu, Y.: Data mining using high performance data clouds: Experimental studies using sector and sphere. CoRR (2008), http://arxiv.org/abs/0808.3019
Iosup, A.: CAMEO: Continuous analytics for massively multiplayer online games on cloud resources. In: Lin, H.-X., Alexander, M., Forsell, M., Knüpfer, A., Prodan, R., Sousa, L., Streit, A. (eds.) Euro-Par 2009. LNCS, vol. 6043, pp. 289–299. Springer, Heidelberg (2010)
Iosup, A.: POGGI: Puzzle-based Online Games on Grid Infrastructures. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 390–403. Springer, Heidelberg (2009)
Iosup, A.: POGGI: generating puzzle instances for online games on grid infrastructures. Concurrency and Computation: Practice and Experience (2010) (accepted April 2010, in print), doi: 10.1002/cpe.1638
Iosup, A., Dumitrescu, C., Epema, D.H.J., Li, H., Wolters, L.: How are real grids used? the analysis of four grid traces and its implications. In: IEEE/ACM International Conference on Grid Computing (GRID), pp. 262–269. IEEE, Los Alamitos (2006)
Iosup, A., Ostermann, S., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. on Parallel and Distrib. Sys. (2010) (accepted September 2010, in print)
Iosup, A., Yigitbasi, N., Epema, D.: On the performance variability of production cloud services. Tech.Report, TU Delft (2010), pds.twi.tudelft.nl/reports/2010/PDS-2010-002.pdf
Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D.: Dryad: distributed data-parallel programs from sequential building blocks. In: EuroSys, pp. 59–72. ACM, New York (2007)
Kondo, D., Javadi, B., Iosup, A., Epema, D.: The Failure Trace Archive: Enabling comparative analysis of failures in diverse distributed systems. In: IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID), pp. 398–407 (2010), Archive data available: http://fta.inria.fr
Lee, H.T., Leonard, D., Wang, X., Loguinov, D.: Irlbot: Scaling to 6 billion pages and beyond. ACM Transactions on the Web (TWEB) 3(3) (2009)
Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 462–470. ACM, New York (2008)
Lin, Y.R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L.: Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Huai, J., Chen, R., Hon, H.W., Liu, Y., Ma, W.Y., Tomkins, A., Zhang, X. (eds.) International Conference on the World Wide Web (WWW), pp. 685–694. ACM, New York (2008)
Menczer, F., Pant, G., Srinivasan, P.: Topical web crawlers: Evaluating adaptive algorithms. ACM Transactions on Internet Technology (TOIT) 4(4), 378–419 (2004)
Miles, S., Groth, P.T., Deelman, E., Vahi, K., Mehta, G., Moreau, L.: Provenance: The bridge between experiments and data. Computing in Science and Engineering 10(3), 38–46 (2008)
Muniswamy-Reddy, K.K., Macko, P., Seltzer, M.I.: Making a cloud provenance-aware. In: Workshop on the Theory and Practice of Provenance. USENIX (2009)
Nae, V., Iosup, A., Podlipnig, S., Prodan, R., Epema, D.H.J., Fahringer, T.: Efficient management of data center resources for massively multiplayer online games. In: ACM/IEEE Conference on High Performance Networking and Computing (SC). IEEE/ACM (2008)
Natarajan, R., Sion, R., Phan, T.: A grid-based approach for enterprise-scale data mining. Future Generation Comp. Syst. 23(1), 48–54 (2007)
Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D.: The eucalyptus open-source cloud-computing system. In: IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID), pp. 124–131 (2009)
Ostermann, S., Iosup, A., Yigitbasi, M.N., Prodan, R., Fahringer, T., Epema, D.: An early performance analysis of cloud computing services for scientific computing. In: CloudComp. LNICST, vol. 34, pp. 1–10. Springer, Heidelberg (2009)
Posea, V., Balint, M., Dimitriu, A., Iosup, A.: An analysis of the BBO Fans online social gaming community. In: RoEduNet International Conference (RoEduNet), pp. 218–223. IEEE, Los Alamitos (2010)
Provost, F.J., Kolluri, V.: A survey of methods for scaling up inductive algorithms. Data Min. Knowl. Discov. 3(2), 131–169 (1999)
Steinkuehler, C., Williams, D.: Where everybody knows your (screen) name: Online games as ”third places”. In: DIGRA Conf. (2005)
Stutzbach, D., Rejaie, R., Sen, S.: Characterizing unstructured overlay topologies in modern p2p file-sharing systems. IEEE/ACM Trans. Netw. 16(2), 267–280 (2008)
Williams, D., Yee, N., Caplan, S.: Who plays, how much, and why? debunking the stereotypical gamer profile. Journal of Computer-Mediated Communication 13(4), 993–1018 (2008)
Woodcock, B.S.: An analysis of mmog subscription growth. Online Report (2006), http://www.mmogchart.com (November 2008)
Yee, N.: The demographics, motivations, and derived experiences of users of massively multi-user online graphical environments. Presence 15(3), 309–329 (2006)
Yu, H., Vahdat, A.: Efficient numerical error bounding for replicated network services. In: VLDB 2000: Proceedings of the 26th International Conference on Very Large Data Bases, pp. 123–133. Morgan Kaufmann Publishers Inc., San Francisco (2000)
Yu, Y., Isard, M., Fetterly, D., Budiu, M., Erlingsson, Ú., Gunda, P.K., Currey, J.: Dryadlinq: A system for general-purpose distributed data-parallel computing using a high-level language. In: OSDI, pp. 1–14. USENIX (2008)
Iosup, A., Lascateu, A., Tapus, N.: CAMEO: Enabling Social Networks for Massively Multiplayer Online Games through Continuous Analytics and Cloud Computing. In: Workshop on Network and System Support for Games (NETGAMES), vol. 7, pp. 1–6. IEEE Press, Piscataway (2010)
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Iosup, A., Lăscăteu, A. (2011). Clouds and Continuous Analytics Enabling Social Networks for Massively Multiplayer Online Games. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_12
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DOI: https://doi.org/10.1007/978-3-642-20344-2_12
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