Budget-Constrained Service Allocation Optimization for Mobile Edge Computing
Yan Ding, Kenli Li, Chubo Liu, Zhuo Tang, Keqin Li · IEEE Transactions on Services Computing · 2021
The service resource allocation strategy optimization problem has always been a hot issue in mobile edge computing (MEC). In this paper, the problem is formulated as a long-term quality of service (QoS) improvement problem while satisfying the budget of MEC service provider (MSP). Since it is very unrealistic to accurately obtain the request information of user equipments (UEs) over a long time, we first transform the original problem into a series of real-time linear programing sub-problems by using Lyapunov optimization method, and propose a centralized algorithm to determine the resource allocation strategies. However, since the sub-problems are still NP-hard problems, it is a huge challenge to determine the strategies for all UEs with the centralized algorithm in a large scale MEC environment. Thus, we then formulate the sub-problems as an N players non-cooperative game, prove that there exists a Nash equilibrium, and develop two iterative algorithms to find the Nash equilibrium while determining the strategies. Experimental results show that the algorithms can take into account QoS and budget of MSP at the same time, and perform better compared to five other common schemes.