ARISE: A Multitask Weak Supervision Framework for Network Measurements
ARISE: A Multitask Weak Supervision Framework for Network Measurements
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DOI：
10.1109/jsac.2022.3180783
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发表时间：
2022-08
影响因子：
16.4
通讯作者：
Jared Knofczynski;Ramakrishnan Durairajan;W. Willinger
中科院分区：
文献类型：
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作者：
Jared Knofczynski;Ramakrishnan Durairajan;W. Willinger
应用机器学习（ML）来缓解网络相关问题，对研究人员和运营商都提出了重大挑战。首先，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，在其他领域流行的标记技术不适合。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），并且随着任务数量的增加，导致训练时间成倍增加。第三，网络运营商采用ML取决于模型提供决策过程基本推理的能力。为了应对这些挑战，我们提出了ARISE，一个多任务的网络测量弱监督框架。ARISE使用基于弱监督的数据编程来标记大规模的网络数据，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，并减少总体训练时间。使用社区数据集，我们表明，ARISE可以生成MTL模型，与多个单任务学习（STL）模型相比，其分类精度有所提高。我们还报告的调查结果表明，承诺的MTL模型提供了一种手段，推理他们的决策过程中，至少在个人任务的水平。
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.