The application of machine learning (ML) to mitigate network-related problems poses significant challenges for researchers and operators alike. For one, there is a general lack of labeled training data in networking, and labeling techniques popular in other domains are ill-suited due to the scarcity of operators’ domain expertise. Second, network problems are typically multi-tasked in nature, requiring multiple ML models (one per task) and resulting in multiplicative increases in training times as the number of tasks increases. Third, the adoption of ML by network operators hinges on the models’ ability to provide basic reasoning about their decision-making procedures. To address these challenges, we propose ARISE, a multi-task weak supervision framework for network measurements. ARISE uses weak supervision-based data programming to label network data at scale and applies learning paradigms such as multi-task learning (MTL) and meta-learning to facilitate information sharing between tasks as well as reduce overall training time. Using community datasets, we show that ARISE can generate MTL models with improved classification accuracy compared to multiple single-task learning (STL) models. We also report findings that show the promise of MTL models for providing a means for reasoning about their decision-making process, at least at the level of individual tasks.

中文翻译：

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ARISE：用于网络测量的多任务弱监督框架

应用机器学习 (ML) 来缓解网络相关问题给研究人员和运营商等带来了重大挑战。一方面，网络中普遍缺乏标记的训练数据，并且由于运营商的领域专业知识稀缺，其他领域流行的标记技术并不适用。其次，网络问题本质上通常是多任务的，需要多个 ML 模型（每个任务一个），随着任务数量的增加，训练时间会成倍增加。第三，网络运营商采用 ML 取决于模型提供有关其决策过程的基本推理的能力。为了应对这些挑战，我们提出了 ARISE，一种用于网络测量的多任务弱监督框架。ARISE 使用基于弱监督的数据编程来大规模标记网络数据，并应用多任务学习 (MTL) 和元学习等学习范式来促进任务之间的信息共享并减少整体训练时间。使用社区数据集，我们表明与多个单任务学习 (STL) 模型相比，ARISE 可以生成具有更高分类准确性的 MTL 模型。我们还报告了一些发现，这些发现表明 MTL 模型有望提供一种推理其决策过程的方法，至少在单个任务的层面上是这样。我们表明，与多个单任务学习 (STL) 模型相比，ARISE 可以生成具有更高分类精度的 MTL 模型。我们还报告了一些发现，这些发现表明 MTL 模型有望提供一种推理其决策过程的方法，至少在单个任务的层面上是这样。我们表明，与多个单任务学习 (STL) 模型相比，ARISE 可以生成具有更高分类精度的 MTL 模型。我们还报告了一些发现，这些发现表明 MTL 模型有望提供一种推理其决策过程的方法，至少在单个任务的层面上是这样。