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A general AI-defined attention network for predicting CDN performance

机译：由AI定义的通用注意力网络，用于预测CDN性能

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摘要

It has become an increasingly popular trend to replicate network content across a geographically distributed structure. The Content Delivery Network (CDN) is a prime example. Content providers have been struggling to improve the Quality of Experience (QoE) of its clients via accurate predictions of CDN performance, which is a crucial prerequisite for subsequent policies like resource scheduling. Methods of Artificial Intelligence (AI) like machine learning and deep learning have shown their advantages on performance predictions recently, but it is still challenging to simultaneously ensure accuracy, generality and low overhead. In this paper, we propose a general AI-defined attention network to make predictions on CDN performance, the results of which can improve CDN resource optimal scheduling. First, we formulate this CDN performance prediction problem as a sequence learning problem. And then we design a two-phase deep neural network with attention so that our model can work well for all cache groups in a CDN. Experiment results show that our model outperforms state-of-art models in accuracy while maintaining a relatively low training overhead. (C) 2019 Elsevier B.V. All rights reserved.
机译：在地理上分散的结构中复制网络内容已成为越来越流行的趋势。内容交付网络（CDN）是一个很好的例子。内容提供商一直在努力通过对CDN性能的准确预测来提高其客户的体验质量（QoE），这是后续策略（如资源调度）的关键前提。诸如机器学习和深度学习之类的人工智能（AI）方法最近在性能预测中显示了其优势，但同时确保准确性，通用性和低开销仍然是挑战。在本文中，我们提出了一个通用的AI定义注意力网络来预测CDN性能，其结果可以改善CDN资源的优化调度。首先，我们将此CDN性能预测问题表述为序列学习问题。然后，我们设计一个两阶段的深度神经网络，以使我们的模型能够很好地适用于CDN中的所有缓存组。实验结果表明，我们的模型在保持相对较低的训练开销的同时，在准确性方面优于最新模型。 （C）2019 Elsevier B.V.保留所有权利。

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