Utilizing the intelligence at the network edge, edge computing paradigm emerges to provide time-sensitive computing services for Internet of Things. In this paper, we investigate sustainable computation offloading in an edge-computing system that consists of energy harvesting-enabled mobile devices (MDs) and a dispatcher. The dispatcher collects computation tasks generated by IoT devices with limited computation power, and offloads them to resourceful MDs in exchange for rewards. We propose an online Rewards-optimal Auction (RoA) to optimize the long-term sum-of-rewards for processing offloaded tasks, meanwhile adapting to the highly dynamic energy harvesting (EH) process and computation task arrivals. RoA is designed based on Lyapunov optimization and Vickrey-Clarke-Groves auction, the operation of which does not require a prior knowledge of the energy harvesting, task arrivals, or wireless channel statistics. Our analytical results confirm the optimality of tasks assignment. Furthermore, simulation results validate the analytical analysis, and verify the efficacy of the proposed RoA.

中文翻译：

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绿色边缘计算系统中计算卸载的近乎最优和真实的在线拍卖

利用网络边缘的智能，边缘计算范式应运而生，为物联网提供时间敏感的计算服务。在本文中，我们研究了边缘计算系统中的可持续计算卸载，该系统由支持能量收集的移动设备 (MD) 和调度程序组成。调度器收集计算能力有限的物联网设备产生的计算任务，并将它们卸载到资源丰富的 MD 以换取奖励。我们提出了一种在线奖励最优拍卖（RoA）来优化处理卸载任务的长期奖励总和，同时适应高度动态的能量收集（EH）过程和计算任务的到达。RoA 是基于 Lyapunov 优化和 Vickrey-Clarke-Groves 拍卖设计的，其操作不需要能量收集、任务到达或无线信道统计的先验知识。我们的分析结果证实了任务分配的最优性。此外，仿真结果验证了分析分析，并验证了所提出的 RoA 的有效性。