The efficiency of a large-scale edge computing system primarily depends on three aspects: i) edge server provision, ii) task migration, and iii) computing resource configuration. In this paper, we study the dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as an online cost minimization problem, aiming to jointly minimize performance degradation and operation expenditure. Due to the stochastic nature, it is an online learning problem with partial feedback. To address it, we derive a deterministic mean field model to approximate the stochastic edge computing system. We show that the mean field model provides the increasingly accurate full feedback as the system scales. We then propose a learning policy based on the mean field model, and show that our proposed policy performs asymptotically as well as the offline optimal configuration. We provide two ways of setting the policy parameters, which achieve a constant competitive ratio (under certain mild conditions) and a sub-linear regret, respectively. Numerical results show that the mean field model significantly improves the convergence speed. Moreover, our proposed policy under the decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead.

中文翻译：

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迈向大规模混合边缘服务器配置：在线平均场学习方法


大规模边缘计算系统的效率主要取决于三个方面：i）边缘服务器供应，ii）任务迁移，iii）计算资源配置。在本文中，我们研究了两种分散任务迁移方案下混合边缘服务器提供的动态资源配置。我们将动态资源配置制定为在线成本最小化问题，旨在共同最小化性能下降和运营支出。由于随机性，它是一个带有部分反馈的在线学习问题。为了解决这个问题，我们推导了一个确定性平均场模型来近似随机边缘计算系统。我们表明，随着系统的扩展，平均场模型提供了越来越准确的完整反馈。然后，我们提出了一种基于平均场模型的学习策略，并表明我们提出的策略与离线最优配置一样渐近执行。我们提供了两种设置策略参数的方法，分别实现恒定的竞争比（在某些温和条件下）和次线性遗憾。数值结果表明，平均场模型显着提高了收敛速度。此外，我们在去中心化任务迁移方案下提出的政策大大降低了运营成本（降低了 23%），并且几乎不产生通信开销。