TY - JOUR
T1 - An incentive-aware job offloading control framework for multi-access edge computing
AU - Li, Lingxiang
AU - Quek, Tony Q.S.
AU - Ren, Ju
AU - Yang, Howard H.
AU - Chen, Zhi
AU - Zhang, Yaoxue
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61901528, in part by the Project under Grant B18059, in part by the SUTD-ZJU Research Collaboration under Grant SUTD-ZJU/RES/01/ 2016, and in part by the SUTD-ZJU Research Collaboration under Grant SUTD-ZJU/RES/05/2016.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - This paper considers a scenario in which an access point (AP) is equipped with a server of finite computing power, and serves multiple resource-hungry users by charging users a price. This price helps to regulate users' behavior in offloading jobs to the AP. However, existing works on pricing are based on abstract concave utility functions, giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individuals interest and the other maximizing the overall systems interest. We analyze the structural property of the games and admit in closed-form the Nash Equilibrium and the Social Equilibrium for the homogeneous user case, respectively. The proposed expressions are functions of user parameters such as the weights of time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal price-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.
AB - This paper considers a scenario in which an access point (AP) is equipped with a server of finite computing power, and serves multiple resource-hungry users by charging users a price. This price helps to regulate users' behavior in offloading jobs to the AP. However, existing works on pricing are based on abstract concave utility functions, giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individuals interest and the other maximizing the overall systems interest. We analyze the structural property of the games and admit in closed-form the Nash Equilibrium and the Social Equilibrium for the homogeneous user case, respectively. The proposed expressions are functions of user parameters such as the weights of time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal price-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.
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U2 - 10.1109/TMC.2019.2941934
DO - 10.1109/TMC.2019.2941934
M3 - Article
AN - SCOPUS:85097784931
VL - 20
SP - 63
EP - 75
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
SN - 1536-1233
IS - 1
M1 - 8840978
ER -