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Game-Based Multi-MD with QoS Computation Offloading for Mobile Edge Computing of Limited Computation Capacity

  • Junyan Hu
  • Chubo LiuEmail author
  • Kenli Li
  • Keqin LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)

Abstract

Mobile edge computing (MEC) is becoming a promising paradigm of providing cloud computing capabilities to the edge network, which can serve mobile devices (MDs) with computation-intensive and delay-sensitive tasks. Facing with high requirements of many MDs, it’s essential for MEC with limited computation capacity to serve more MDs with QoS. For each mobile device, it is also desirable to have a low energy consumption with an expected deadline. To solve above problems, we propose a Game-based Computation Offloading (GCO) algorithm, which includes the task offloading profile and the transmission power controlling with the method of non-cooperative game. Our mechanism maximizes the number of served MDs with deadline, as well as minimizing the energy consumption of each MD whose task is executed on MEC. Specifically, Given the allocation of transmission power, a Greedy-Pruning algorithm is proposed to determine the number of tasks executed on MEC. Besides, each MD adopts his/her transmission power controlling strategy to compete the computation resource of MEC or minimize the energy consumption. A game model for illustrating the problem of task offloading is formulated to find a proper transmission power for each task and is proved the existence of Nash equilibrium solution. Experiments are simulated to evaluate the proposed algorithm in terms of effectiveness evaluation.

Keywords

Mobile edge computing Nash equilibrium Non-cooperative game theory Task offloading Power controlling 

Notes

Acknowledgments

The research was partially funded by the National Key R&D Program of China (Grant No. 2018YFB1003401), the Program of National Natural Science Foundation of China (Grant No. 61751204).

References

  1. 1.
    Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018)CrossRefGoogle Scholar
  2. 2.
    Porambage, P., Okwuibe, J., Liyanage, M., Taleb, T., Ylianttila, M.: Survey on multi-access edge computing for internet of things realization. IEEE Commun. Surv. Tutor. 20, 2961–2991 (2018) CrossRefGoogle Scholar
  3. 3.
    Ning, Z., Wang, X., Huang, J.: Mobile edge computing-enabled 5G vehicular networks: toward the integration of communication and computing. IEEE Veh. Technol. Mag. 14, 54–61 (2018)CrossRefGoogle Scholar
  4. 4.
    Kai, W., Hao, Y., Wei, Q., Min, G.: Enabling collaborative edge computing for software defined vehicular networks. IEEE Netw. 32, 112–117 (2018)Google Scholar
  5. 5.
    Guo, H., Liu, J.: Collaborative computation offloading for multiaccess edge computing over fibercwireless networks. IEEE Trans. Veh. Technol. 67(5), 4514–4526 (2018)CrossRefGoogle Scholar
  6. 6.
    Chen, W., Dong, W., Li, K.: Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans. Serv. Comput. 99, 1 (2018) Google Scholar
  7. 7.
    Yang, L., Zhang, H., Ming, L., Guo, J., Hong, J.: Mobile edge computing empowered energy efficient task offloading in 5G. IEEE Trans. Veh. Technol. 67, 6398–6409 (2018)CrossRefGoogle Scholar
  8. 8.
    Feng, W., et al.: Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans. Wirel. Commun. 17(3), 1784–1797 (2017)Google Scholar
  9. 9.
    Min, C., Hao, Y.: Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J. Sel. Areas Commun. 36(3), 587–597 (2018)CrossRefGoogle Scholar
  10. 10.
    Qiang, F., Ansari, N.: Application aware workload allocation for edge computing based IoT. IEEE Internet Things J. 5(3), 2146–2153 (2018)CrossRefGoogle Scholar
  11. 11.
    Liu, J., Mao, Y., Zhang, J., Letaief, K.B.: Delay-optimal computation task scheduling for mobile-edge computing systems. In: IEEE International Symposium on Information Theory, April 2016Google Scholar
  12. 12.
    Xiang, S., Ansari, N.: Latency aware workload offloading in the cloudlet network. IEEE Commun. Lett. 21(7), 1481–1484 (2017)CrossRefGoogle Scholar
  13. 13.
    Jiao, Z., et al.: Energy-latency trade-off for energy-aware offloading in mobile edge computing networks. IEEE Internet Things J. 5, 2633–2645 (2018)CrossRefGoogle Scholar
  14. 14.
    Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24, 2795–2808 (2016)CrossRefGoogle Scholar
  15. 15.
    Rodrigues, T.G., Suto, K., Nishiyama, H., Kato, N., Temma, K.: Cloudlets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration. IEEE Trans. Comput. 67, 1287–1300 (2018)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Mao, Y., Zhang, J., Letaief, K.B.: Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In: Wireless Communications and Networking Conference (2017)Google Scholar
  17. 17.
    Tao, X., Ota, K., Dong, M., Qi, H., Li, K.: Performance guaranteed computation offloading for mobile-edge cloud computing. IEEE Wirel. Commun. Lett. 6(6), 774–777 (2017)CrossRefGoogle Scholar
  18. 18.
    Xu, C., Lei, J., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2016)CrossRefGoogle Scholar
  19. 19.
    Hu, X., Wong, K.K., Yang, K.: Wireless powered cooperation-assisted mobile edge computing. IEEE Trans. Wirel. Commun. 17(4), 2375–2388 (2018)CrossRefGoogle Scholar
  20. 20.
    Li, K.: A game theoretic approach to computation offloading strategy optimization for non-cooperative users in mobile edge computing. IEEE Trans. Sustain. Comput. 99, 1 (2018)Google Scholar
  21. 21.
    Ranadheera, S., Maghsudi, S., Hossain, E.: Computation offloading and activation of mobile edge computing servers: a minority game. IEEE Wirel. Commun. Lett. 7, 688–691 (2018)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina
  2. 2.National Supercomputing Center in ChangshaChangshaChina
  3. 3.Department of Computer ScienceState University of New YorkNew PaltzUSA

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