Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for task computation, regarded as total cost. Then, we formulate a total cost minimization problem to design the bandwidth allocation and task allocation, while considering realistic heterogenous delay requirements of the computational tasks. Due to the binary task allocation variable, the formulated optimization problem is nonconvex. Therefore, we solve the bandwidth allocation and task allocation problem by decoupling the original optimization problem into bandwidth allocation and task allocation subproblems. As the bandwidth allocation problem is a convex optimization problem, we first determine the bandwidth allocation for given task allocation strategy, followed by conceiving the traditional convex optimization strategy to obtain the bandwidth allocation solution. Based on the asymptotic property of received signal-to-interference-plus-noise ratio (SINR) under the cell-free massive MIMO setting and bandwidth allocation solution, we formulate a dual problem to solve the task allocation subproblem by relaxing the binary constraint with Lagrange partial relaxation for heterogenous task delay requirements. At last, simulation results are provided to demonstrate that our proposed task offloading scheme performs better than the benchmark schemes, where the minimum-cost optimal offloading strategy for heterogeneous delay requirements of the computational tasks may be controlled by the asymptotic property of the received SINR in our proposed cell-free massive MIMO-aided multi-tier computing systems.
翻译:多层计算可以通过多层计算节点提高任务计算性能。本文提出了一种采用多层计算节点的无细胞 massive MIMO 辅助计算系统,以提高计算性能。首先,我们研究了任务计算的计算延迟和总能耗,将其视为总成本。然后,我们制定了一个总成本最小化的问题,以设计带宽分配和任务分配,同时考虑计算任务的实际异构延迟要求。由于任务分配变量是二进制的,所以所制定的优化问题是非凸的。因此,我们通过将原始优化问题分解为带宽分配和任务分配子问题来解决带宽分配和任务分配问题。由于带宽分配问题是一个凸优化问题,我们首先确定给定任务分配策略的带宽分配,然后想出传统的凸优化策略,以获得带宽分配解。根据无细胞 massive MIMO 环境下接收信干噪比(SINR)的渐近性质和带宽分配解,我们制定了一个双重问题,通过拉格朗日部分松弛来放松二进制约束以解决任务分配子问题,以满足异构任务延迟需求。最后,提供了模拟结果,以证明我们提出的任务卸载方案比基准方案表现更好,在异构延迟要求的计算任务的最小成本最优卸载策略可以通过我们提出的无细胞 massive MIMO 辅助多层计算系统中接收到的 SINR 的渐近性质来控制。