This letter investigates a cache-enabled multiuser mobile edge computing (MEC) system with dynamic task arrivals, taking into account the impact of proactive cache placement on the system's overall energy consumption. We consider that an access point (AP) schedules a wireless device (WD) to offload computational tasks while executing the tasks of a finite library in the \emph{task caching} phase, such that the nearby WDs with the same task request arriving later can directly download the task results in the \emph{task arrival and execution} phase. We aim for minimizing the system's weighted-sum energy over a finite-time horizon, by jointly optimizing the task caching decision and the MEC execution of the AP, and local computing as well as task offloading of the WDs at each time slot, subject to caching capacity, task causality, and completion deadline constraints. The formulated design problem is a mixed-integer nonlinear program. Under the assumption of fully predicable task arrivals, we first propose a branch-and-bound (BnB) based method to obtain the optimal offline solution. Next, we propose two low-complexity schemes based on convex relaxation and task-popularity, respectively. Finally, numerical results show the benefit of the proposed schemes over existing benchmark schemes.

翻译：本文研究了一种支持缓存的、具有动态任务到达的多用户移动边缘计算（MEC）系统，重点分析了主动缓存布局对系统整体能耗的影响。我们考虑接入点（AP）调度一个无线设备（WD）卸载计算任务，同时在“任务缓存”阶段执行有限库中的任务，使得后续到达的、具有相同任务请求的邻近WD能够在“任务到达与执行”阶段直接下载任务结果。我们的目标是在有限时间范围内最小化系统的加权总能耗，通过在每个时隙联合优化AP的任务缓存决策与MEC执行、WD的本地计算及任务卸载，同时满足缓存容量、任务因果性和完成截止时间约束。所构建的设计问题是一个混合整数非线性规划。在任务到达完全可预测的假设下，我们首先提出一种基于分支定界（BnB）的方法以获得最优离线解。随后，我们分别提出两种低复杂度方案，分别基于凸松弛和任务流行度。最后，数值结果表明所提方案相较于现有基准方案具有明显优势。