Bayesian Optimization for Online Management in Dynamic Mobile Edge Computing
Bayesian Optimization for Online Management in Dynamic Mobile Edge Computing
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DOI：
10.1109/twc.2023.3307875
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发表时间：
2022-11
影响因子：
10.4
通讯作者：
Jiahe Yan;Qin Lu;G. Giannakis
中科院分区：
文献类型：
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作者：
Jiahe Yan;Qin Lu;G. Giannakis
近年来，移动的边缘计算（MEC）在包括物联网（IoT）的硬件受限的无线设备（WD）的计算能力的成本有效的增强的前提下出现。在一般的多服务器多用户MEC系统中，每个WD具有要执行的计算任务，并且必须选择二进制（卸载）加载决策，沿着以在线方式的模拟幅度资源分配变量，目标是最小化具有动态系统状态的总能量延迟成本（EDC）。虽然过去的工作通常依赖于EDC函数的显式表达，但本贡献考虑了实际设置，其中代替系统状态信息，EDC函数不以分析形式提供，而是仅显示查询点处的函数值。为了解决这样一个具有挑战性的在线组合问题，只有强盗信息，新的贝叶斯优化（BO）的方法提出了利用多臂强盗（MAB）框架。每个时隙，首先通过MAB方法获得离散卸载决策，随后使用BO选择规则优化模拟资源分配变量。通过利用时间和上下文信息，两种新的BO方法，称为时变BO和上下文时变BO，开发。数值试验验证了所提出的BO方法的优点相比，当代的基准在不同的MEC网络规模。
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.