A Reverse Auction Model for Efficient Resource Allocation in Mobile Edge Computation Offloading

Abstract

Mobile edge computing (MEC) enables mobile users to offload their computationally-intensive tasks to the servers located at the network's edge. One of the fundamental challenges of MEC is to develop methods to efficiently allocate the limited computational resources of the edge servers to the offloading users. To address this challenge, in this paper, we propose a reverse auction framework based on position auction consisting of pricing, bidding strategy optimization, and winner determination. The proposed solution allows the edge servers to maximize their utility through strategic participation. Moreover, it ensures users' satisfaction by taking the users' preferences into account. The solution has polynomial-time complexity and enjoys desirable economical characteristics including envy-free and individual rationality. In addition to the theoretical analysis, numerical results establish the sound performance of the proposed framework in terms of the system's resource utilization as well as the users' satisfaction level.