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Learning-Based Pricing for Privacy-Preserving Job Offloading in Mobile Edge Computing
Conference proceeding

Learning-Based Pricing for Privacy-Preserving Job Offloading in Mobile Edge Computing

Lingxiang Li, Marie Siew, Tony QS Quek and Marie Therese Hui Lin Siew
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.4784
01/01/2019

Abstract

Acoustics Economic models Edge computing Learning Mobile computing Optimization Pricing Signal processing
Conference Title: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Conference Start Date: 2019, May 12 Conference End Date: 2019, May 17 Conference Location: Brighton, United Kingdom This paper considers a scenario in which an access point (AP) is equipped with a mobile edge server (MEC) of finite computing power, and serves multiple resource-hungry mobile users by charging users a price. This price helps to regulate users’ behavior in offloading computation jobs to the AP. To that end, first we introduce an economics model for MEC bearing physical layer offloading intuition. We then propose a learning based pricing mechanism, in which with no direct control and no knowledge of users’ private information, the AP learns the optimal price. Under our mechanism, the AP induces self-interested users to make socially optimal offloading decisions, thus maximizing the system-wide welfare.

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