This paper discusses incentive mechanism design for collaborative task offloading in mobile edge computing (MEC). Different from most existing work in the literature that was based on offline settings, in this paper, an online truthful mechanism integrating computation and communication resource allocation is proposed. In our system model, upon the arrival of a smartphone user who requests task offloading, the base station (BS) needs to make a decision right away without knowing any future information on i) whether to accept or reject this task offloading request and ii) if accepted, who to execute the task (the BS itself or nearby smartphone users called collaborators). By considering each task’s specific requirements in terms of data size, delay, and preference, we formulate a social-welfare-maximization problem, which integrates collaborator selection, communication and computation resource allocation, transmission and computation time scheduling, as well as pricing policy design. To solve this complicated problem, a novel online mechanism is proposed based on the primal-dual optimization framework. Theoretical analyses show that our mechanism can guarantee feasibility, truthfulness, and computational efficiency (competitive ratio of 3). We further use comprehensive simulations to validate our analyses and the properties of our proposed mechanism.

中文翻译：

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移动边缘计算协同任务卸载的在线激励机制

本文讨论了移动边缘计算（MEC）中协同任务卸载的激励机制设计。与文献中大多数基于离线设置的现有工作不同，本文提出了一种集成计算和通信资源分配的在线真实机制。在我们的系统模型中，当请求任务卸载的智能手机用户到来时，基站 (BS) 需要立即做出决定，而无需知道关于 i) 是否接受或拒绝此任务卸载请求以及 ii) 的任何未来信息如果被接受，谁来执行任务（BS 本身或附近的智能手机用户称为合作者）。通过考虑每个任务在数据大小、延迟和偏好方面的具体要求，我们制定了一个社会福利最大化问题，它集协作者选择、通信和计算资源分配、传输和计算时间调度以及定价策略设计于一体。为了解决这个复杂的问题，提出了一种基于原始对偶优化框架的新型在线机制。理论分析表明，我们的机制可以保证可行性、真实性和计算效率（竞争比为 3）。我们进一步使用综合模拟来验证我们的分析和我们提出的机制的特性。理论分析表明，我们的机制可以保证可行性、真实性和计算效率（竞争比为 3）。我们进一步使用综合模拟来验证我们的分析和我们提出的机制的特性。理论分析表明，我们的机制可以保证可行性、真实性和计算效率（竞争比为 3）。我们进一步使用综合模拟来验证我们的分析和我们提出的机制的特性。