Characteristic of User Generated Load in Mobile Gaming Environment

  • Krzysztof Grochla
  • Wojciech Borczyk
  • Maciej Rostanski
  • Rafal Koffer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 391)


This paper describes an analysis of the variability of the load imposed by users of a mobile gaming platform. We measure the communication between the sample mobile gaming application and characterize the types of requests that are being transmitted to the cloud service. We characterize the variability of the load in time, finding weekly and daily patterns of load and differences between working days and weekends of approximately 20 %. We analyze the correlations between the processing of the different types of requests, and find that the processing time correlates with the total load observed on server, but is only partially related to the type of request being processed.


Mobile gaming Performance evaluation Sesion length Load estimation 



This work is in part supported by Polish National Centre for Research and Development under the grant No. INNOTECH-K3/HI3/20/228040/NCBR/14.


  1. 1.
    Ahn, H., Wijaya, M.E., Esmero, B.C.: A systemic smartphone usage pattern analysis: focusing on smartphone addiction issue. Int. J. Multimedia Ubiquit. Eng. 9(6) (2014)Google Scholar
  2. 2.
    Böhmer, M., Hecht, B., Schöning, J., Krüger, A., Bauer, G.: Falling asleep with angry birds, facebook and kindle: a large scale study on mobile application usage. In: MobileHCI 2011, pp. 47–56. Stockholm (2011)Google Scholar
  3. 3.
    Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wireless Commun. Mobile Comput. 13(18), 1587–1611 (2013)CrossRefGoogle Scholar
  4. 4.
    Domańska, J., Domańska, A., Czachórski, T.: A few investigations of long-range dependence in network traffic. In: Czachórski, T., Gelenbe, E., Lent, R. (eds.) Information Sciences and Systems 2014, pp. 137–144. Springer, Switzerland (2014)Google Scholar
  5. 5.
    Farago, P.: Flurry Presents Apps by the Numbers (2011).
  6. 6.
    Farago, P.: The Truth About Cats and Dogs: Smartphone vs Tablet Usage Differences (2012).
  7. 7.
    Ferreira, D., Goncalves, J., Kostakos, V., Barkhuus, L., Dey, A.K.: Contextual experience sampling of mobile application micro-usage. In: MobileHCI 2014, pp. 91–100. Toronto, Canada (2014)Google Scholar
  8. 8.
    Gorawski, M., Grochla, K.: The real-life mobility model: RLMM. In: FGCT 2013, pp. 201–206. London (2013)Google Scholar
  9. 9.
    Gorawski, M., Grochla, K.: Review of mobility models for performance evaluation of wireless networks. In: Gruca, A., Czachorski, T., Kozielski, S. (eds.) Man-Machine Interactions 3, AISC, vol. 242, pp. 567–577. Springer, Switzerland (2014)CrossRefGoogle Scholar
  10. 10.
    Huang, J., Qian, F., Gerber, A., Mao, Z.M., Sen, S., Spatscheck, O.: A close examination of performance and power characteristics of 4G LTE networks. In: MobiSys 2012, pp. 225–238. Low Wood Bay (2012)Google Scholar
  11. 11.
    Incuvo: Createrria,
  12. 12.
  13. 13.
    Rostański, M., Buchwald, P., Arkadiusz, J.: Relative and non-relative databases performance with an android platform application. Theor. Appl. Inform. 25(3–4), 224–238 (2013)Google Scholar
  14. 14.
    Rostanski, M., Grochla, K., Seman, A.: Evaluation of highly available and fault-tolerant middleware clustered architectures using RabbitMQ. In: FedCSIS 2014, pp. 879–884. Poland (2014)Google Scholar
  15. 15.
    Satyanarayanan, M.: Fundamental challenges in mobile computing. In: PODC 1996, pp. 1–7. Philadelphia (1996)Google Scholar
  16. 16.
    Shin, C., Hong, J.H., Dey, A.K.: Understanding and prediction of mobile application usage for smart phones. In: Ubicomp 2012, pp. 173–182. Pittsburgh (2012)Google Scholar
  17. 17.
    Skelley, T., Namoun, A., Mehandjiev, N.: The impact of a mobile information system on changing travel behaviour and improving travel experience. In: Mobile Web Information Systems, pp. 233–247. Springer (2013)Google Scholar
  18. 18.
    Yan, T., Chu, D., Ganesan, D., Kansal, A., Liu, J.: Fast app launching for mobile devices using predictive user context. In: MobiSys 2012, pp. 113–126. Low Wood Bay (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Krzysztof Grochla
    • 1
  • Wojciech Borczyk
    • 2
  • Maciej Rostanski
    • 3
  • Rafal Koffer
    • 2
  1. 1.Institute of Theoretical and Applied InformaticsPASGliwicePoland
  2. 2.IncuvoKatowicePoland
  3. 3.University of Dabrowa GorniczaDa̧browa GórniczaPoland

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