Nonlinear Online Incentive Mechanism Design in Edge Computing Systems with Energy Budget

Gang Li, Jun Cai, Xianfu Chen, Zhou Su

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this paper, we consider task ofoading in edge computing systems, where tasks are ofoaded by the base station to resourceful mobile users. With the consideration of unique characteristics in practical edge computing systems, such as dynamic arrival of computation tasks, and energy constraints at battery-powered mobile users, we formulate an incentive mechanism design problem by jointly optimizing task ofoading decisions, and allocation of both communications (i.e., power and bandwidth), and computation resources. In order to tackle the nonlinear issue in the designed mechanism, a novel online incentive mechanism is proposed. We rst convert the original mechanism design problem into several one-shot design problems by temporally removing the energy constraint. Then, we propose a new mechanism design framework, called the Integrate Rounding Scheme based Maxima-in-distributional Range (IRSM), and based on that, design a new incentive mechanism for each one-shot problem. Finally, we reconsider energy constraints to design a new nonlinear online incentive mechanism by rationally combining the previously derived one-shot ones. Theoretical analyses show that our proposed nonlinear online incentive mechanism can guarantee individual rationality, truthfulness, a sound competitive ratio, and computational efciency. We further conduct comprehensive simulations to validate the effectiveness and superiority of our proposed mechanism.

Original languageEnglish
JournalIEEE Transactions on Mobile Computing
DOIs
Publication statusAccepted/In press - 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Computational modeling
  • Delays
  • Edge computing
  • online nonlinear incentive mechanism
  • Optimization
  • Resource management
  • Servers
  • social welfare maximization
  • Task analysis
  • task offloading

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