期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 8, 页码 8080-8091出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3090935
关键词
Delays; Pricing; Edge computing; Optimization; Task analysis; Servers; Computational modeling; Multi-access edge computing; differentiated services; priority pricing; decentralized mechanism
资金
- National Natural Science Foundation of China [61901528]
- Hunan Natural Science Foundation [2020JJ5769]
- National Research Foundation, Singapore
- Infocomm Media Development Authority under its Future Communications Research & Development Programme
- MOE ARF Tier 2 [T2EP20120-0006]
The article discusses incentive mechanisms based on priority pricing in multi-access edge computing, proposing two pricing schemes: one based on known user profit functions and another based on partial knowledge.
Multi-Access edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by selfish users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Nevertheless, different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose a partial-knowledge based pricing mechanism where no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency.
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