Abstract
Mobile cloud gaming (MCG) can provide users with high-quality gaming services anytime, anywhere, but suffers from long network latency and huge wide-area traffic. In order to solve these problems, mobile edge computing (MEC) is envisioned as a promising approach to enable relevant computing at the edge. Since the quality of experience (QoE) of the game requires high frame rates and low network latency, the placement of service entities can affect the performance of MEC-enabled MCG. In addition, users have a high degree of mobility while enjoying MCG, so service migration is proposed to reduce QoE impairment, and service migration means an increase in system cost. To address these challenges, we investigate the service placement of MEC-enabled MCG. Considering the dynamics of the system, we propose to minimize the QoE impairment according to the constraint cost of migration. We design the ECP algorithm to solve the problem.
This work was supported in part by the National Natural Science Foundation of China under Grant 61662052, in part by the Natural Science Foundation of Inner Mongolia Autonomous Region under Grant 2021MS06002, in part by the Science and Technology Planning Project of Inner Mongolia Autonomous Region under Grant 2021GG0155, and in part by the Major Research Plan of Inner Mongolia Natural Science Foundation under Grant 2019ZD15.
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References
Wu, D., Ke, Y., He, J., Li, Y., Chen, M.: Mobile cloud gaming. In: Springer Encyclopedia of Computer Graphics and Games, pp. 1–7 (2017)
Choy, S., Wong, B., Simon, G., Rosenberg, C.: A hybrid edge-cloud architecture for reducing on-demand gaming latency. Multimedia Syst. 20(5), 503–519 (2014). https://doi.org/10.1007/s00530-014-0367-z
Huang, C.-Y., Hsu, C.-H., Chen, D.-Y., Chen, K.-T.: Quantifying user satisfaction in mobile cloud games. In: ACM Proceedings of Workshop on Mobile Video Delivery, pp. 1–6 (2014)
A. Nadembega, A. S. Hafid and R. Brisebois: Mobility prediction model-based service migration procedure for follow me cloud to support QoS and QoE. In: Proceedings of the IEEE International Conference on Communications (ICC), pp. 1–6 (2016)
Ksentini, A., Taleb, T., Chen, M.: A markov decision process-based service migration procedure for follow me cloud. In: Proceedings of the IEEE International Conference on Communications (ICC) (2014)
Ceselli, A., Premoli, M., Secci, S.: Mobile edge cloud network design optimization. IEEE/ACM Trans. Netw. 25(3), 1818–1831 (2017)
Liao, X., et al.: LiveRender: a cloud gaming system based on compressed graphics streaming. IEEE/ACM Trans. Networking 24(4), 2128–2139 (2016)
Wang, S., Xu, J., Zhang, N., Liu, Y.: A survey on service migration in mobile edge computing. IEEE Access 6, 23511–23528 (2018)
Neely, M.J.: Stochastic network optimization with application to communication and queueing systems. Synth. Lect. Commun. Netw. 3(1), 1–211 (2010)
Ouyang, T., Li, R., Chen, X., Zhou, Z., Tang, X.: Adaptive user-managed service placement for mobile edge computing: an online learning approach. In: Proceedings of the IEEE Conference on Computer Communications, pp. 1468–1476 (2019)
Cao, T., Qian, Z., Wu, K., Zhou, M., Jin, Y.: Service placement and bandwidth allocation for MEC-enabled mobile cloud gaming. In: Proceedings of the 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 179–188 (2021)
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Gao, Y., Xu, Z. (2022). Improving Gaming Experience with Dynamic Service Placement in Mobile Edge Computing. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_50
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DOI: https://doi.org/10.1007/978-3-031-19211-1_50
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