The edge intelligence (EI) has been widely applied recently. Spliting the model between device, edge server, and cloud can improve the performance of EI greatly. The model segmentation without user mobility has been investigated deeply by previous works. However, in most use cases of EI, the end devices are mobile. Only a few works have been carried out on this aspect. These works still have many issues, such as ignoring the energy consumption of mobile device, inappropriate network assumption, and low effectiveness on adaptiving user mobility, etc. Therefore, for addressing the disadvantages of model segmentation and resource allocation in previous works, we propose mobility and cost aware model segmentation and resource allocation algorithm for accelerating the inference at edge (MCSA). Specfically, in the scenario without user mobility, the loop interation gradient descent (Li-GD) algorithm is provided. When the mobile user has a large model inference task needs to be calculated, it will take the energy consumption of mobile user, the communication and computing resource renting cost, and the inference delay into account to find the optimal model segmentation and resource allocation strategy. In the scenario with user mobility, the mobiity aware Li-GD (MLi-GD) algorithm is proposed to calculate the optimal strategy. Then, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation ratio. The experimental results demonstrate the effectiveness of the proposed algorithms.

翻译：边缘智能（EI）近年来被广泛应用。在设备、边缘服务器和云之间拆分模型可以显著提升EI性能。已有研究深入探讨了无用户移动性的模型分割问题，但在大多数EI应用场景中，终端设备具有移动性。目前仅少数研究涉及这一方面，且仍存在诸多问题，例如忽略移动设备的能耗、网络假设不恰当、对用户移动性的适应性不佳等。为此，针对现有工作在模型分割与资源分配上的不足，我们提出了一种面向边缘推理加速的移动性与成本感知模型分割与资源分配算法（MCSA）。具体而言，在无用户移动性场景下，提出了循环迭代梯度下降（Li-GD）算法。当移动用户需要计算大规模模型推理任务时，该算法综合考虑移动设备能耗、通信与计算资源租用成本以及推理时延，寻找最优模型分割与资源分配策略。在存在用户移动性的场景下，提出了移动性感知循环迭代梯度下降（MLi-GD）算法以计算最优策略。随后，研究了所提算法的收敛性、复杂度和近似比等性质。实验结果验证了所提算法的有效性。