论文标题
动态移动边缘计算中用于在线管理的贝叶斯优化
Bayesian Optimization for Online Management in Dynamic Mobile Edge Computing
论文作者
论文摘要
近年来，在包含物联网（IoT）的硬件受限无线设备（WDS）的计算能力上提高了具有成本效益的计算能力的前提。在一般的多服务器多用户MEC系统中，每个WD都有一个计算任务可以执行，并且必须以在线方式选择二进制（OFF）加载决策，以及模拟振幅资源分配变量，目的是最大程度地降至最小化动态系统状态的整体能量 - 列表成本（EDC）。尽管过去的作品通常依赖于EDC函数的明确表达，但本贡献考虑了一个实用的环境，在代替系统状态信息的情况下，EDC函数以分析形式不可用，而仅显示查询点的功能值。通过仅利用多武器强盗（MAB）框架来提出基于新颖的贝叶斯优化（BO）方法，以解决这种具有挑战性的在线组合问题，基于新颖的贝叶斯优化（BO）方法。每个时间插槽首先通过MAB方法获得离散的卸载决策，然后使用BO选择规则对模拟资源分配变量进行了优化。通过利用时间和上下文信息，开发了两种新颖的BO方法，称为时变的BO和上下文变化的BO。与不同MEC网络大小的当代基准相比，数值测试验证了所提出的BO方法的优点。
Recent years have witnessed the emergence of mobile edge computing (MEC), on the premise of a cost-effective enhancement in the computational ability of hardware-constrained wireless devices (WDs) comprising the Internet of Things (IoT). In a general multi-server multi-user MEC system, each WD has a computational task to execute and has to select binary (off)loading decisions, along with the analog-amplitude resource allocation variables in an online manner, with the goal of minimizing the overall energy-delay cost (EDC) with dynamic system states. While past works typically rely on the explicit expression of the EDC function, the present contribution considers a practical setting, where in lieu of system state information, the EDC function is not available in analytical form, and instead only the function values at queried points are revealed. Towards tackling such a challenging online combinatorial problem with only bandit information, novel Bayesian optimization (BO) based approaches are put forth by leveraging the multi-armed bandit (MAB) framework. Per time slot, the discrete offloading decisions are first obtained via the MAB method, and the analog resource allocation variables are subsequently optimized using the BO selection rule. By exploiting both temporal and contextual information, two novel BO approaches, termed time-varying BO and contextual time-varying BO, are developed. Numerical tests validate the merits of the proposed BO approaches compared with contemporary benchmarks under different MEC network sizes.
