In this paper, we consider a task offloading problem in a multi-access edge computing (MEC) network, in which edge users can either use their local processing unit to compute their tasks or offload their tasks to a nearby edge server through multiple communication channels each with different characteristics. The main objective is to maximize the energy efficiency of the edge users while meeting computing tasks deadlines. In the multi-user multi-channel offloading scenario, users are distributed with partial observations of the system states. We formulate this problem as a stochastic optimization problem and leverage \emph{contextual neural multi-armed bandit} models to develop an energy-efficient deadline-aware solution, dubbed E2DA. The proposed E2DA framework only relies on partial state information (i.e., computation task features) to make offloading decisions. Through extensive numerical analysis, we demonstrate that the E2DA algorithm can efficiently learn an offloading policy and achieve close-to-optimal performance in comparison with several baseline policies that optimize energy consumption and/or response time. Furthermore, we provide a comprehensive set of results on the MEC system performance for various applications such as augmented reality (AR) and virtual reality (VR).

翻译：本文研究多接入边缘计算（MEC）网络中的任务卸载问题。在该网络中，边缘用户既可使用本地处理单元计算任务，也可通过多个特性各异的通信信道将任务卸载至邻近边缘服务器。核心目标是在满足计算任务截止时间的前提下，最大化边缘用户的能量效率。在多用户多信道卸载场景中，用户对系统状态仅有部分观测能力。我们将此问题建模为随机优化问题，并利用上下文神经多臂赌博机模型开发了一种名为E2DA的能效与截止时间感知解决方案。所提出的E2DA框架仅依赖部分状态信息（即计算任务特征）进行卸载决策。通过大量数值分析证明，与若干以能耗和/或响应时间优化为目标的基准策略相比，E2DA算法能够高效学习卸载策略并实现接近最优的性能。此外，我们针对增强现实（AR）和虚拟现实（VR）等不同应用，提供了MEC系统性能的综合结果集。