Game-Based Multi-MD with QoS Computation Offloading for Mobile Edge Computing of Limited Computation Capacity
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.
KeywordsMobile edge computing Nash equilibrium Non-cooperative game theory Task offloading Power controlling
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).
- 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
- 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
- 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
- 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
- 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
- 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