Truthful Deep Mechanism Design for Revenue-Maximization in Edge Computing With Budget Constraints

In this paper, collaborative task offloading in edge computing is studied, where computation requesters can offload tasks to not only the edge server, but also nearby smartphone users. By considering the fact that smartphone users may not always be willing to provide such computation service because of the consumption of their own energy and resources, a truthful mechanism is designed to provide incentive to smartphone users. The design aims to maximize the net revenue of the service provider and addresses more practical, but more complicated, scenarios of unknown a prior distribution information on smartphone users' private information. To tackle this high computational complexity, which makes the traditional mechanism design methods infeasible, a new approach, called truthful deep mechanism, is proposed by leveraging a multi-task machine learning model, where inherently inter-connected collaborator selection and pricing policy determination are decided by designing two deep neural networks. The numerical results show that the proposed deep truthful mechanism can ensure a convergence to a stable state and can satisfy all required economical properties, including individual rationality, incentive compatibility, and budget balance.

Automated summary

This paper studies collaborative task offloading in edge computing, proposes a truthful mechanism to incentivize smartphone users, and introduces a new approach to tackle the high computational complexity.