Pricing-based resource allocation in three-tier edge computing for social welfare maximization

Abstract

Edge computing is a promising computing paradigm for Internet of Everything and AI-driven applications where substantial computing resources are pushed to the edge of the network in close proximity to the end users. Unlike most of the existing works concentrating on system-side metrics such as job response time, we study how the entities in edge computing interact with each other. Specifically, we study a three-tier edge computing market that consists of edge servers, brokers, and edge users, where brokers are introduced to connect edge servers and edge users, and to facilitate resource deployment and maintenance for edge users. Our goal is to maximize social welfare. The uniqueness of this market, such as the agents’ private information and selfishness, prevents one from using standard optimization techniques. Therefore, we propose a pricing-based resource allocation mechanism via iterative bidding, called MECM, for the three-tier edge computing market. Our theoretical results show that MECM converges to the social optimum with a provable convergence rate of O(1k), where k is the number of iterations, and has desirable properties, i.e., budget balance and individual rationality. Our extensive simulations validate MECM’s performance and its properties in various scenarios.