A two-tier cooperative computing system is considered in this article, where tier-1 consists of multiple mobile edge computing (MEC) servers and tier-2 consists of one mobile cloud computing (MCC) server. We assume that multiple mobile devices (MDs) are allowed to offload their tasks to the MEC and MCC servers. To facilitate task execution for the MDs, both types of servers are deployed with convolutional neural networks (CNNs). We assume that the cooperation between the MEC servers and the MCC server in task execution is enabled by scheduling different layers of the CNNs. To achieve efficient information interaction and task management, we first design a joint task management architecture. Stressing the importance of task execution latency, we formulate the joint task offloading, CNN layer scheduling and resource allocation optimization problem as an overall task latency minimization problem. To solve the formulated optimization problem, we transform it to three subproblems, i.e., CNN layer scheduling subproblem, task offloading subproblem and resource allocation subproblem, and solve the subproblems, respectively, by means of the extensive search algorithm, reformulation-linearization-technique and Lagrange dual algorithm. The effectiveness of the proposed algorithm is demonstrated via numerical simulations.

中文翻译：

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协作计算系统中的联合任务分载，CNN层调度和资源分配

本文考虑了两层协作计算系统，其中第1层由多个移动边缘计算（MEC）服务器组成，而第2层由一个移动云计算（MCC）服务器组成。我们假设允许多个移动设备（MD）将其任务卸载到MEC和MCC服务器。为了促进MD的任务执行，两种类型的服务器都部署有卷积神经网络（CNN）。我们假设通过调度CNN的不同层来启用任务执行中MEC服务器和MCC服务器之间的协作。为了实现有效的信息交互和任务管理，我们首先设计一个联合任务管理体系结构。强调任务执行延迟的重要性，我们制定了联合任务分流，CNN层调度和资源分配优化问题作为总体任务等待时间最小化问题。为了解决公式化的优化问题，我们将其转换为三个子问题，即CNN层调度子问题，任务卸载子问题和资源分配子问题，并分别通过广泛的搜索算法，重构线性化技术和拉格朗日对偶算法。通过数值仿真证明了该算法的有效性。重构线性化技术和Lagrange对偶算法。通过数值仿真证明了该算法的有效性。重构线性化技术和Lagrange对偶算法。通过数值仿真证明了该算法的有效性。