Auction design for cross-edge task offloading in heterogeneous mobile edge clouds

计算机科学 双重拍卖 云计算 反向拍卖 移动边缘计算 拍卖算法 分布式计算 拍卖理论 服务器 水准点(测量) 任务(项目管理) GSM演进的增强数据速率 共同价值拍卖 计算机网络 收入等值 微观经济学 操作系统 人工智能 经济 管理 地理 大地测量学
作者
Weifeng Lü,Wei Wu,Jia Xu,Pengcheng Zhao,Dejun Yang,Lijie Xu
出处
期刊:Computer Communications [Elsevier BV]
卷期号:181: 90-101 被引量:11
标识
DOI:10.1016/j.comcom.2021.09.035
摘要

Task offloading is a promising technology to exploit the available resources in edge cloud efficiently. Many incentive mechanisms for offloading systems have been proposed. However, most of existing works study the centralized incentive mechanisms under the assumption that all mobile edge infrastructures are operated by a central cloud. In this paper, we aim to design the auction-based truthful incentive mechanisms for heavily loaded task offloading system in heterogeneous MECs. We first study the homogeneous MEC situation and present a global auction executed in the central cloud as a benchmark. For the heterogeneous MEC situation, we model the system as a dual auction framework, which enables the heterogeneous MECs to perform cross-edge task offloading without the participation of central servers. Specifically, we design two dual auction models: secondary auction-based model, which enables the system to offload tasks from a large-scale region in a single auction, and double auction-based model, which is suitable for the time sensitive tasks. Then the auctions for these two dual auction models are proposed. Through rigorous theoretical analysis, we demonstrate that the proposed auctions achieve desirable properties of computational efficiency, individual rationality, budget balance, truthfulness, and guaranteed approximation. The simulation results show that the secondary auction and double auction can obtain 14.5% and 4.2% more social welfare than comparison algorithm on average, respectively. In addition, the double auction has great advantage in terms of computation efficiency.
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