利用用户多样性实现高能效的边缘协作雾计算
Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Collaborative Fog Computing
摘要：
受设备上协作神经网络推理等应用的启发，这项工作研究了边缘促进的协作雾计算-其中边缘设备相互协作并与网络边缘协作以完成处理任务-以增强单个边缘设备的计算能力，同时优化协作以提高能效。协作计算使用Map-Reduce分布式计算框架建模，包括由通信阶段分开的两轮计算。考虑到边缘设备在计算和通信能力方面的多样性，计算负载在边缘设备之间进行了最佳分配。此外，边缘设备的本地参数（如CPU时钟频率和RF发射功率）也针对能效进行了优化。相应的优化问题可以被证明是凸的，最优性条件可以通过拉格朗日对偶理论得到。一个注水式的解释分配给每个边缘设备的计算负载的大小。数值实验表明，所提出的最佳协同计算方案的好处比其他各种计划在几个方面。最值得注意的是，所提出的方案表现出成功处理较重计算和/或较小延迟的概率增加，以及高达两个数量级的能效增益。这两项改进都来自于该方案最佳利用边缘设备多样性的能力。
英文摘要：
Motivated by applications such as on-device collaborative neural network inference, this work investigates edge-facilitated collaborative fog computing - in which edge-devices collaborate with each other and with the edge of the network to complete a processing task - to augment the computing capabilities of individual edge-devices while optimizing the collaboration for energy-efficiency. Collaborative computing is modeled using the Map-Reduce distributed computing framework, consisting in two rounds of computations separated by a communication phase. The computing load is optimally distributed among the edge-devices, taking into account their diversity in term of computing and communications capabilities. In addition, edge-devices local parameters such as CPU clock frequency and RF transmit power are also optimized for energy-efficiency. The corresponding optimization problem can be shown to be convex and optimality conditions can be obtained through Lagrange duality theory. A waterfilling-like interpretation for the size of the computing load assigned to each edge-device is given. Numerical experiments demonstrate the benefits of the proposed optimal collaborative-computing scheme over various other schemes in several respects. Most notably, the proposed scheme exhibits increased probability of successfully dealing with heavier computations and/or smaller latency along with energy-efficiency gains of up to two orders of magnitude. Both improvements come from the scheme ability to optimally leverage edge-devices diversity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
