Caching of popular content closer to the mobile user can significantly increase overall user experience as well as network efficiency by decongesting backbone network segments in the case of congestion episodes. In order to find the optimal caching locations, many conventional approaches rely on solving a complex optimization problem that suffers from the curse of dimensionality, which may fail to support online decision making. In this paper we propose a framework to amalgamate model based optimization with data driven techniques by transforming an optimization problem to a grayscale image and train a convolutional neural network (CNN) to predict optimal caching location policies. The rationale for the proposed modelling comes from CNN's superiority to capture features in grayscale images reaching human level performance in image recognition problems. The CNN is trained with optimal solutions and numerical investigations reveal that the performance can increase by more than 400% compared to powerful randomized greedy algorithms. To this end, the proposed technique seems as a promising way forward to the holy grail aspect in resource orchestration which is providing high quality decision making in real time.

中文翻译：

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使用深度卷积神经网络将缓存作为图像表征问题

将流行内容缓存在靠近移动用户的位置可以通过在发生拥塞事件时缓解骨干网段的拥塞来显着提高整体用户体验和网络效率。为了找到最佳缓存位置，许多传统方法依赖于解决受维度灾难影响的复杂优化问题，这可能无法支持在线决策。在本文中，我们提出了一个框架，通过将优化问题转换为灰度图像并训练卷积神经网络 (CNN) 来预测最佳缓存位置策略，从而将基于模型的优化与数据驱动技术相结合。建议建模的基本原理来自 CNN' 在灰度图像中捕捉特征的优越性，在图像识别问题中达到人类水平。CNN 使用最优解进行训练，数值研究表明，与强大的随机贪婪算法相比，其性能可以提高 400% 以上。为此，所提出的技术似乎是朝着资源编排中的圣杯方面迈进的有希望的方式，它可以实时提供高质量的决策。