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

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 (EPRA) 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, EPRA 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 article, we model the interaction between the two sides as a Stackelberg game. First, given a specified EPRA 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., user-granular EPRA and task-granular EPRA 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.

I. Introduction

With the rapid development of IoT technology, massive amounts of data are generated in distributed sensors and widespread mobile devices [1]. Due to the computing power and resource limitations of IoT devices, it is impossible to process all the data in the local devices. Instead, the traditional cloud-based architecture is widely used with its strong computation power to handle mage-scale data and complex tasks such as model training in deep neural networks. However, uploading data of such a large scale to the cloud may induce serious transmission burdens, which results in unacceptable response delay and expensive energy cost.