Predicting Throughput of Cloud Network Infrastructure Using Neural Networks
Predicting Throughput of Cloud Network Infrastructure Using Neural Networks
复制标题
使用神经网络预测云网络基础设施的吞吐量
DOI：
10.1109/infocomwkshps51825.2021.9484520
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Mike Truty
中科院分区：
文献类型：
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作者：
Derek Phanekham;S. Nair;N. Rao;Mike Truty
网络基础设施的吞吐量预测是网络容量规划、调度、资源管理、路径选择等网络功能的重要方面。在本白皮书中，我们描述了通过支持全球云计算的网络基础设施收集的吞吐量测量结果。我们训练深度学习模型来使用这些测量来预测TCP吞吐量，这些测量显示了缓冲区调优和并行流的性能改进。我们还比较了机器学习和传统方法在预测公共云环境中的单线程和多流吞吐量方面的准确性。
Throughput prediction of network infrastructures is an important aspect of capacity planning, scheduling, resource management, route selection and other network functions. In this paper, we describe throughput measurements collected over a network infrastructure that supports cloud computing spanning the globe. We train deep learning models to predict TCP throughput using these measurements, which show performance improvements with buffer tuning and parallel streams. We also compare the accuracy of machine learning and conventional methods in predicting both single thread and mutli-stream throughput in a public cloud environment.