Collaborative computation offloading in mobile edge computing where edge users offload tasks opportunistically to resourceful neighboring mobile devices (MDs), offers a promising solution to satisfy low-latency requirements. However, most existing works assume that those MDs volunteer to help edge users without an incentive mechanism. In this article, we propose an auction-based incentive mechanism, where users and MDs participate in the system dynamically. Our auction mechanism runs in the online fashion and optimizes the long-term system welfare without knowledge of future information, e.g., task start time, task length, resource demand, and valuation, etc. We prove that the proposed online mechanism achieves the desired properties, including individual rationality, truthfulness, and computational tractability. Moreover, the theoretical competitive ratio shows that our online mechanism achieves near-optimal long-term social welfare close to the offline optimum. Extensive experiments based on real-world traces demonstrate the efficiency of the proposed online mechanism.

中文翻译：

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移动边缘计算中协作计算卸载的真实在线机制


移动边缘计算中的协作计算卸载（边缘用户机会性地将任务卸载到资源丰富的相邻移动设备（MD））提供了一种有前途的解决方案来满足低延迟要求。然而，大多数现有的工作都假设这些 MD 自愿帮助边缘用户，但没有激励机制。在本文中，我们提出了一种基于拍卖的激励机制，用户和 MD 动态参与系统。我们的拍卖机制以在线方式运行，并在不了解未来信息（例如任务开始时间、任务长度、资源需求和估值等）的情况下优化长期系统福利。我们证明所提出的在线机制实现了预期的属性，包括个人理性、真实性和计算易处理性。此外，理论竞争比表明，我们的在线机制实现了接近离线最优的近乎最优的长期社会福利。基于现实世界痕迹的大量实验证明了所提出的在线机制的效率。