Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a "best-effort" at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. As a component of these two methods, we develop a novel coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks.
翻译:设备到设备( D2D) 通信预计将是边缘网络中分布分布式计算的关键助推器。 提供这一能力的一个关键挑战是需要明智地管理边缘存在的各种通信和计算资源,以满足处理需求。 在本文件中,我们开发了一种最佳方法,将网络地形与设备和网络资源分配结合起来,以尽量减少全部D2D管理费用,我们用时间和能源来量化这些管理任务处理所需的全部D2D管理费用。我们模型中的变量包括任务分配、 CPU分配、 亚通道选择和多输入多输出(MIMO)无线装置的成型设计。我们建议了两种方法来解决由此产生的非凝聚混合整型程序。我们建议了两种方法:半详尽的搜索优化,它代表了获得最佳解决方案的“最佳”和高效的替代优化,而这种优化在计算上效率更高。作为这两种方法的一个组成部分,我们开发了一种新式协调的算法,它显示我们为通用接收器特性获得了最佳的状态。 通过数字实验,我们发现我们的方法在网络中产生了实质性的改进, 并且与本地的优化方法相比,可以进一步优化, 与局部的计算和优化的计算。