TY - JOUR
T1 - Pricing-based resource allocation in three-tier edge computing for social welfare maximization
AU - Li, Yupeng
AU - Xia, Mengjia
AU - Duan, Jingpu
AU - Chen, Yang
N1 - Funding Information:
This work is supported by Guangdong Basic and Applied Basic Research Foundation (Ref. 2022A1515011583 ), One-off Tier 2 Start-up Grant (2020/2021) of Hong Kong Baptist University (Ref. RC-OFSGT2/20-21/COMM/002 ), Startup Grant (Tier 1) for New Academics AY2020/21 of Hong Kong Baptist University , the AI-Info Communication Study (AIS) Scheme 2021/22 (Ref. AIS 21-22/06 ), National Natural Science Foundation of China (No. 61902171 and No. 62072115 ), CCF-DiDi Gaia Collaborative Research Fund for Young Scholar by China Computer Federation (CCF) and Didi Chuxing Technology Co. (Didi) , the Major Key Project of PCL ( PCL2021A15 ), and Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness (No. HNTS2022010 ).
Publisher Copyright:
© 2022
PY - 2022/11/9
Y1 - 2022/11/9
N2 - 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 [Formula presented], 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.
AB - 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 [Formula presented], 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.
KW - Edge and fog computing
KW - IoT networks
KW - Network economics and games
KW - Network resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85137547498&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2022.109311
DO - 10.1016/j.comnet.2022.109311
M3 - Journal article
SN - 1389-1286
VL - 217
JO - Computer Networks
JF - Computer Networks
M1 - 109311
ER -