Profitable and Scalable MEC: Reputation-Based Service Replication via Stackelberg Game

Mobile edge computing (MEC) is a promising paradigm for Internet of Things applications requiring synchronized user experiences. However, sustaining scalable and reliable MEC services is challenging when computational resources are overloaded, especially as MEC service providers (SPs) must minimize operational costs to maximize profits while offering competitively priced services. This article proposes the cooperative multiprovider market (CMPM) scheme, the first to cooperatively enhance service scalability and reliability while addressing the profit-pricing dilemma in a multiprovider market. CMPM enables overloaded home SPs (HSPs) to leverage underutilized computational resources from reliable foreign SPs (FSPs) via reputation-based service replication, meeting the stringent Quality of Service (QoS) requirements for real-time applications involving user groups. CMPM resolves the pricing dilemma by applying a game-theoretic approach, allowing FSPs to dynamically optimize revenue and adjust prices when HSPs cannot meet user demand. We formulate the resource allocation and pricing problem as a Stackelberg game, establish the existence of the equilibrium, and develop a distributed algorithm to reach it. Extensive evaluations show that CMPM significantly reduces unit prices, attracts more HSPs, and better manages high-density user loads compared to state-of-the-art schemes that overlook SP reputation and social welfare. CMPM also achieves up to 84% higher FSP revenue, a 67% improvement in scalability, and a 70% higher task success rate compared to baseline schemes.

I. Introduction

The widespread proliferation of the Internet of Things (IoT) applications that rely on synchronized experiences and strict Quality of Service (QoS) requirements for user groups is anticipated to place significant demands on computing resources [1]. Online gaming, live sports events, and concerts, which necessitate real-time synchronization, are particularly affected by these demands. Considering the intensive workloads inherent in these applications, it is crucial to develop efficient computing solutions and use adequate computing paradigms that can meet the stringent QoS requirements while also ensuring high scalability [2].