Applications such as virtual reality and online gaming require low delays for acceptable user experience. A key task for over-the-top (OTT) service providers who provide these applications is sending traffic through the networks to minimize delays. OTT traffic is typically generated from multiple data centers which are multi-homed to several network ingresses. However, information about the path characteristics of the underlying network from the ingresses to destinations is not explicitly available to OTT services. These can only be inferred from external probing. In this paper, we combine network tomography with machine learning to minimize delays. We consider this problem in a general setting where traffic sources can choose a set of ingresses through which their traffic enter a black box network. The problem in this setting can be viewed as a reinforcement learning problem with strict linear constraints on a continuous action space. Key technical challenges to solving this problem include the high dimensionality of the problem and handling constraints that are intrinsic to networks. Evaluation results show that our methods achieve up to 60% delay reductions in comparison to standard heuristics. Moreover, the methods we develop can be used in a centralized manner or in a distributed manner by multiple independent agents.

中文翻译：

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基于层析成像的不透明网络负荷分配学习

虚拟现实和在线游戏等应用程序需要低延迟才能获得可接受的用户体验。提供这些应用程序的OTT服务提供商的一项关键任务是通过网络发送流量，以最大程度地减少延迟。OTT流量通常是由多个数据中心生成的，这些数据中心被多宿主到几个网络入口。但是，有关底层网络从入口到目的地的路径特性的信息对于OTT服务而言不是明确可用的。这些只能从外部探测中推断出来。在本文中，我们将网络断层扫描与机器学习相结合，以最大程度地减少延迟。我们在一般情况下考虑此问题，在这种情况下，流量源可以选择一组入口，以使其流量进入黑盒网络。可以将这种情况下的问题视为对连续动作空间具有严格线性约束的强化学习问题。解决此问题的关键技术挑战包括问题的高度维度和网络固有的处理约束。评估结果表明，与标准启发式方法相比，我们的方法最多可将延迟减少60％。而且，我们开发的方法可以由多个独立代理以集中方式或分布式方式使用。评估结果表明，与标准启发式方法相比，我们的方法最多可将延迟减少60％。而且，我们开发的方法可以由多个独立代理以集中方式或分布式方式使用。评估结果表明，与标准启发式方法相比，我们的方法最多可将延迟减少60％。而且，我们开发的方法可以由多个独立代理以集中方式或分布式方式使用。