Truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive Applications in Edge Clouds

Abstract

With the rapid advancement of wireless networks, edge computing has emerged as a promising paradigm for providing computing services to nearby latency-sensitive applications. Toward this trend, resource trading markets among mobile users (MUs) and edge clouds are emerging which need well-designed allocation and pricing mechanisms. In this paper, we propose a double auction-based multi-market resource trading framework (DAMRT), which integrates the dynamic programming and padding-based double auction methods, aiming to achieve approximate social welfare maximization and guarantee the properties of truthfulness and budget balance in the constrained edge computing system. To be specific, for admission control among different base station (BS) central markets, a dynamic programming-based method is proposed to obtain the optimal BS-MU association set. For each market, a truthful padding-based double auction resource allocation mechanism (TPDA) is proposed, which uses a linear programming-based padding method and a binary search algorithm to obtain the near-optimal allocation solution, and leverages a critical-value and a VCG-based pricing strategy for winning MUs and service providers. Our theoretical analysis proves that TPDA achieves truthfulness, individual rationality, and budget balance. Furthermore, simulation results verify the effectiveness of DAMRT.

This work is supported by the National Key R&D Program of China under Grant No. 2022YFB4500800; the National Natural Science Foundation of China under Grants No. 92267206 and No. 62032013.