Sample Transfer Optimization with Adaptive Deep Neural Network
Sample Transfer Optimization with Adaptive Deep Neural Network
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DOI：
10.1109/indis49552.2019.00013
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Hemanta Sapkota;M. Arifuzzaman;Engin Arslan
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文献类型：
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作者：
Hemanta Sapkota;M. Arifuzzaman;Engin Arslan
应用层传输配置在在高速网络中实现理想的性能中起着至关重要的作用。但是，找到给定传输任务的最佳配置是一个困难的问题，因为它取决于各种因素，包括数据集特征，网络设置和背景流量。最新的传输调谐解决方案依赖于实时样品转移来评估各种配置并估算最佳配置。但是，现有的运行样品转移的方法会产生高延迟和测量误差，因此显着限制了转移调谐算法的效率。在本文中，我们引入了自适应饲料向前深神经网络（DNN），以最大程度地降低样品转移的错误率，而不会增加其执行时间。我们在四个不同的高速网络中运行了115k文件传输，并使用其日志来训练自适应DNN，该DNN可以通过分析瞬时吞吐量值快速而准确地预测样品转移的吞吐量。与最先进的解决方案相比，提出的模型在各种网络中收集的结果表明，提出的模型可将错误率降低多达50％，同时保持执行时间较低。我们还表明，可以通过调整模型的超参数来进一步降低延迟或错误率，以满足用户或应用程序的特定需求。最后，转移学习分析表明，一个网络中开发的模型将在具有相似传输收敛特征的其他网络中产生准确的结果，从而减轻了为每个网络运行广泛的数据收集和模型推导工作的需求。
Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error rate of sample transfers without increasing their execution time. We ran 115K file transfers in four different high-speed networks and used their logs to train an adaptive DNN that can quickly and accurately predict the throughput of sample transfers by analyzing instantaneous throughput values. The results gathered in various networks with rich set of transfer configurations indicate that the proposed model reduces error rate by up to 50% compared to the state-of-the-art solutions while keeping the execution time low. We also show that one can further reduce delay or error rate by tuning hyperparameters of the model to meet specific needs of user or application. Finally, transfer learning analysis reveals that the model developed in one network would yield accurate results in other networks with similar transfer convergence characteristics, alleviating the needs to run an extensive data collection and model derivation efforts for each network.