A Stackelberg-Game-Based Framework for Edge Pricing and Resource Allocation in Mobile Edge Computing

斯塔克伯格竞赛 计算机科学 移动边缘计算 GSM演进的增强数据速率 资源配置 博弈论 资源管理(计算) 边缘计算 移动电话技术 移动计算 分布式计算 计算机网络 移动无线电 电信 微观经济学 经济
作者
Siyao Cheng,Tian Ren,Hao Zhang,Jiayan Huang,Jie Liu
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:11 (11): 20514-20530 被引量:1
标识
DOI:10.1109/jiot.2024.3372016
摘要

Nowadays, Mobile Edge Computing (MEC) appears as a new computing paradigm with its ability to utilize the computing power of both local devices and edge servers. In MEC, edge pricing and resource allocation are two important problems. Edge servers make a profit by selling computing services to users. To maximize their revenue, they need to determine an appropriate price for each user, and decide the amount of resources allocated to each user. However, none of the existing works consider the effect of users' task assignment strategy on the revenue of the edge. In fact, edge pricing and resource allocation will affect the users' task offloading decision, as they expect to minimize their total cost. In turn, the users' decision will also influence the revenue of the edge. Therefore, the interaction between mobile users and edge servers should be considered carefully and the interests of both sides need to be maximized simultaneously. In this paper, we model the interaction between the two sides as a Stackelberg game. First, given a specified edge pricing and resource allocation strategy, we derive a near-optimal task assignment strategy for each user to minimize the total cost based on a greedy algorithm UTA-G. Then, by applying the backward induction method, two pricing and resource allocation schemes with different granularity, i.e., EPRA-U and EPRA-T are proposed to bring higher revenue to the edge. Experimental results demonstrate that all the proposed algorithms can have good performance in task-intensive, resource-deficient and workload-heavy scenarios.
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