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MPaaS: Mobility prediction as a service in telecom cloud

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Abstract

Mobile applications and services relying on mobility prediction have recently spurred lots of interest. In this paper, we propose mobility prediction based on cellular traces as an infrastructural level service of telecom cloud. Mobility Prediction as a Service (MPaaS) embeds mobility mining and forecasting algorithms into a cloud-based user location tracking framework. By empowering MPaaS, the hosted 3rd-party and value-added services can benefit from online mobility prediction. Particularly we took Mobility-aware Personalization and Predictive Resource Allocation as key features to elaborate how MPaaS drives new fashion of mobile cloud applications. Due to the randomness of human mobility patterns, mobility predicting remains a very challenging task in MPaaS research. Our preliminary study observed collective behavioral patterns (CBP) in mobility of crowds, and proposed a CBP-based mobility predictor. MPaaS system equips a hybrid predictor fusing both CBP-based scheme and Markov-based predictor to provide telecom cloud with large-scale mobility prediction capacity.

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Notes

  1. In this paper we use the word “telecom operator cloud” and “telecom cloud” interchangeably.

  2. The user #4, #8, #12, #23, #6, #69, #102, #37, #9, #26, #25, #27, #35, #73 and #53 are involved for empirical study.

References

  • Begleiter, R., El-Yaniv, R., Yona, G. (2004). On prediction using variable order markov models. Journal of Artificial Intelligence Research (JAIR), 22, 385–421.

    Google Scholar 

  • Boldrini, C., & Passarella, A. (2010). Modelling spatial and temporal properties of human mobility driven by users’ social relationships. Computer Communications, 33(9), 1056–1074.

    Article  Google Scholar 

  • Calabrese, F., Di Lorenzo, G., Ratti, C. (2010). Human mobility prediction based on individual and collective geographical preferences. In Proceedings IEEE international conference on intelligent transportation systems. Portugal: IEEE.

  • Chen, T.L., Hsu, C.H., Chen, S.C. (2010). Scheduling of job combination and dispatching strategy for grid and cloud system. Advances in Grid and Pervasive Computing. Lecture Notes in Computer Science, 6104, 612–621.

    Article  Google Scholar 

  • Cho, E., Myers, S.A., Leskovec, J. (2011). Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM conference on KDD (pp. 1082–1090). San Diego.

  • Chon, Y., Talipov, E., Shin, H., Cha, H. (2011). Mobility prediction-based smartphone energy optimization for everyday location monitoring. In Proceedings of the 9th ACM conference on embedded networked sensor systems (pp. 82–95). ACM.

  • Chun, B.G., Ihm, S., Maniatis, P., Naik, M., Patti, A. (2011). Clonecloud: Elastic execution between mobile device and cloud. In Proceedings of the 6th conference on Computer systems (pp. 301–314).

  • Church, K., & Smyth, B. (2008). Who, what, where and when: A new approach to mobile search. In Proceedings of the 13th international conference on intelligent user interfaces (p. 309). ACM.

  • Cuervo, E., Balasubramanian, A., Cho, D., Wolman, A., Saroiu, S., Chandra, R., Bahl, P. (2010). Maui: Making smartphones last longer with code offload. In Proceedings of the 8th international conference on Mobile systems, applications, and services (pp. 49–62). ACM.

  • De Serres, Y., & Hegarty, L. (2001). Value-added services in the converged network. IEEE Communications Magazine, 39(9), 146–154.

    Article  Google Scholar 

  • Dinh, H.T., Lee, C., Niyato, D., Wang, P. (2011). A survey of mobile cloud computing: architecture, applications, and approaches. Wireless Communications and Mobile Computing. doi:10.1002/wcm.1203.

    Google Scholar 

  • Drago, I., Mellia, M., Munafò, M.M., Sperotto, A., Sadre, R., Pras, A. (2012). Inside dropbox: understanding personal cloud storage services.

  • Ericsson Discussion Paper (2012). The telecom cloud opportunity. http://www.ericsson.com/res/site_AU/docs/2012/ericsson_telecom_cloud_discussion_paper.pdf.

  • Eugster, P.T., Felber, P.A., Guerraoui, R., Kermarrec, A.M. (2003). The many faces of publish/subscribe. ACM Computing Surveys (CSUR), 35(2), 114–131.

    Article  Google Scholar 

  • Fakoor, R., Raj, M., Nazi, A., Di Francesco, M., Das, S.K. (2012). An integrated cloud-based framework for mobile phone sensing. In Proceedings of the 1st edition of the MCC workshop on mobile cloud computing (pp. 47–52). ACM.

  • Gao, W., Li, Q., Zhao, B., Cao, G. (2009). Multicasting in delay tolerant networks: A social network perspective. In Proceedings of the 10th ACM international symposium on MobiHoc (pp. 299–308). ACM.

  • Goiri, Í., Guitart, J., Torres, J. (2012). Economic model of a cloud provider operating in a federated cloud. Information Systems Frontiers, 14(4), 827–843.

    Article  Google Scholar 

  • Gouveia, F., Wahle, S., Blum, N., Magedanz, T. (2009). Cloud computing and epc/ims integration: New value-added services on demand. In Proceedings of the 5th international ICST mobile multimedia communications conference (p. 51). ICST.

  • Gutierrez-Garcia, J.O., & Sim, K.M. (2012). Ga-based cloud resource estimation for agent-based execution of bag-of-tasks applications. Information Systems Frontiers, 14(4), 925–951.

    Article  Google Scholar 

  • Han, J., Cheng, H., Xin, D., Yan, X. (2007). Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15, 55–86.

    Article  Google Scholar 

  • Hsu, C.H., Chen, S.C., Lee, C.C., Chang, H.Y., Lai, K.C., Li, K.C., Rong, C. (2011). Energy-aware task consolidation technique for cloud computing. In IEEE 3rd international conference on cloud computing technology and science (CloudCom), 2011 (pp. 115–121). IEEE.

  • Hsu, C.H., Cuzzocrea, A., Chen, S.C. (2011). Cad: an efficient data management and migration scheme across clouds for data-intensive scientific applications. Data Management in Grid and Peer-to-Peer Systems. Lecture Notes in Computer Science, 6864, 120–134.

    Article  Google Scholar 

  • Katz, R.H. (2013). CS-294-7: Handoff Strategies, University of UC Berkeley.

  • Klein, A., Mannweiler, C., Schneider, J., Schotten, H. D. (2010). Access schemes for mobile cloud computing. In 11th international conference on mobile data management (MDM), 2010 (pp. 387–392). IEEE.

  • Knightson, K., Morita, N., Towle, T. (2005). Ngn architecture: generic principles, functional architecture, and implementation. IEEE Communications Magazine, 43(10), 49–56.

    Article  Google Scholar 

  • Kosta, S., Aucinas, A., Hui, P., Mortier, R., Zhang, X. (2012). Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In INFOCOM, 2012 Proceedings IEEE (pp. 945–953). IEEE.

  • Kumar, K., & Lu, Y.H. (2010). Cloud computing for mobile users: can offloading computation save energy?Computer, 43(4), 51–56.

    Article  Google Scholar 

  • Kumar, K., Liu, J., Lu, Y.H., Bhargava, B. (2012). A survey of computation offloading for mobile systems. Mobile Networks and Applications, 18, 1–12.

    Google Scholar 

  • Kuo, Y.F., Wu, C.M., Deng, W.J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior, 25(4), 887–896.

    Article  Google Scholar 

  • Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.

    Article  Google Scholar 

  • Lassabe, F., Canalda, P., Chatonnay, P., Spies, F., Center, N.M.D., Charlet, D. (2006). Predictive mobility models based on kth markov models. In IEEE international conference on pervasive services (pp. 303–306).

  • Liu, Y., Yang, Z., Wang, X., Jian, L. (2010). Location, localization, and localizability. Journal of Computer Science and Technology, 25, 274–297.

    Article  Google Scholar 

  • Lu, H., Yang, J., Liu, Z., Lane, N.D., Choudhury, T., Campbell, A.T. (2010). The jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM conference on embedded networked sensor systems (pp. 71–84). ACM.

  • Martens, B., & Teuteberg, F. (2012). Decision-making in cloud computing environments: a cost and risk based approach. Information Systems Frontiers, 14, 1–23.

    Google Scholar 

  • Mazhelis, O., & Tyrväinen, P. (2012). Economic aspects of hybrid cloud infrastructure: user organization perspective. Information Systems Frontiers, 14(4), 845–869.

    Article  Google Scholar 

  • Musolesi, M., & Mascolo, C. (2006). A community based mobility model for ad hoc network research. In Proceedings of the 2nd international workshop on multi-hop ad hoc networks: From theory to reality (pp. 31–38). Florence: ACM.

  • Neumann, D., Bodenstein, C., Rana, O.F., Krishnaswamy, R. (2011). Stacee: Enhancing storage clouds using edge devices. In Proceedings of the 1st ACM/IEEE workshop on Autonomic computing in economics (pp. 19–26). ACM.

  • Pentlandb, A.S., Eaglea, N., Lazerc, D. (2009). Inferring social network structure using mobile phone data. In Proceedings of the National Academy of Sciences (PNAS) (Vol. 106, pp. 15274–15278).

  • Psounis, K., Helmy, A., Hsu, W.J., Spyropoulos, T. (2007). Modeling time-variant user mobility in wireless mobile networks. In Proceedings of the 27th IEEE international conference on computer communications (pp. 758–766). Alaska.

  • Rao, B., & Minakakis, L. (2003). Evolution of mobile location-based services. Communications of the ACM, 46(12), 61–65.

    Article  Google Scholar 

  • Roy, A., Das, S.K., Misra, A. (2004). Exploiting information theory for adaptive mobility and resource management in future cellular networks. IEEE Wireless Communications, 11, 59–65.

    Article  Google Scholar 

  • Saarinen, A., Siekkinen, M., Xiao, Y., Nurminen, J. K., Kemppainen, M., Hui, P. (2011). Offloadable apps using smartdiet: towards an analysis toolkit for mobile application developers. arXiv:1111.3806.

  • Schmidt, D.C., Stal, M., Rohnert, H., Buschmann, F., Wiley, J. (2000). Pattern-oriented software architecture: Patterns for concurrent and networked objects (Vol. 2). Wiley.

  • Sesia, S., Toufik, I., Baker, M. (2009). Lte–the umts long term evolution. From Theory to Practice, published in, 66.

  • Shafer, G. (1976). A mathematical theory of evidence. Princeton University Press.

  • Shankar, P., Huang, Y.W., Castro, P., Nath, B., Iftode, L. (2012). Crowds replace experts: Building better location-based services using mobile social network interactions. In IEEE international conference on pervasive computing and communications (PerCom), 2012 (pp. 20–29). IEEE.

  • Siris, V.A., & Kalyvas, D. (2012). Enhancing mobile data offloading with mobility prediction and prefetching. In Proceedings of the 7th ACM international workshop on mobility in the evolving internet architecture (pp. 17–22). ACM.

  • Soh, W.S., & Kim, H.S. (2003). Qos provisioning in cellular networks based on mobility prediction techniques. IEEE Communications Magazine, 41(1), 86–92.

    Article  Google Scholar 

  • Song, L., Kotz, D., Jain, R., He, X. (2004). Evaluating location predictors with extensive wi-fi mobility data. In Proceedings of INFOCOM 2004 (pp. 1414–1424). IEEE.

  • Song, C., Qu, Z., Blumm, N., Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.

    Article  Google Scholar 

  • Strauss, J., Lesniewski-Laas, C., Paluska, J.M., Ford, B., Morris, R., Kaashoek, F. (2010). Device transparency: a new model for mobile storage. ACM SIGOPS Operating Systems Review, 44(1), 5–9.

    Article  Google Scholar 

  • Stuedi, P., Mohomed, I., Terry, D. (2010). Wherestore: Location-based data storage for mobile devices interacting with the cloud. In Proceedings of the 1st ACM workshop on mobile cloud computing and services: social networks and beyond (p. 1). ACM.

  • Wikipedia (2012). Mobile cloud storage. http://en.wikipedia.org/wiki/Mobile_Cloud_Storage.

  • Wikipedia (2012). Service models of cloud computing. http://en.wikipedia.org/wiki/Infrastructure_as_a_service#Service_models.

  • Xiong, H., Zhang, D., Zhang, D., Gauthier, V. (2012). Predicting mobile phone user locations by exploiting collective behavioral patterns. In Proceedings of the 9th IEEE international conference on ubiquitous intelligence and computing (pp. 164–171). IEEE.

  • Yan, T., Kumar, V., Ganesan, D. (2010). Crowdsearch: Exploiting crowds for accurate real-time image search on mobile phones. In Proceedings of the 8th international conference on mobile systems, applications, and services (pp. 77–90). ACM.

  • Yang, C.T., Chen, W.S., Huang, K.L., Liu, J.C., Hsu, W.H., Hsu, C.H. (2012). Implementation of smart power management and service system on cloud computing. In Proceedings of the 9th IEEE international conference on ubiquitous intelligence and computing (pp. 924–929). IEEE.

  • Zhang, D., Zhou, Z., Zou, Q., Zhan, T., Jo, M. (2012). Asynchronous event detection for context inconsistency in pervasive computing. IJAHUC, 11(4), 195–205.

    Article  Google Scholar 

  • Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q. (2010). Collaborative filtering meets mobile recommendation: A user-centered approach. In Proceedings of the 24th AAAI conference on artificial intelligence.

  • Zhu, C., Li, K., Lv, Q., Shang, L., Dick, R. P. (2009). Iscope: Personalized multi-modality image search for mobile devices. In Proceedings of the 7th international conference on mobile systems, applications, and services (pp. 277–290). ACM.

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Correspondence to Daqing Zhang.

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This work is supported by EU FP7 Project MONICA (No. 295222) and EU FP7 Project SOCIETIES (No. 257493).

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Xiong, H., Zhang, D., Zhang, D. et al. MPaaS: Mobility prediction as a service in telecom cloud. Inf Syst Front 16, 59–75 (2014). https://doi.org/10.1007/s10796-013-9476-z

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