ML-Based Performance Modeling in SDN-Enabled Data Center Networks
ML-Based Performance Modeling in SDN-Enabled Data Center Networks
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DOI：
10.1109/tnsm.2022.3197789
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发表时间：
2023-03
影响因子：
5.3
通讯作者：
Benoit Nougnanke;Y. Labit;M. Bruyère;U. Aïvodji;Simone Ferlin
中科院分区：
文献类型：
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作者：
Benoit Nougnanke;Y. Labit;M. Bruyère;U. Aïvodji;Simone Ferlin
流量优化和智能缓冲是在具有异构工作负载（包括 Incast 和大象流量）的数据中心中实现出色的应用程序性能和资源效率的基础。然而，缺少提供有关各种因素如何影响这些管理功能所需的流量性能指标的见解的通用性能模型。对于incast的特殊情况，现有模型是分析模型，要么与特定协议版本紧密耦合，要么特定于某些经验数据。受这一观察的启发，本文提出了一种数据中心网络中基于 SDN 的基于机器学习的性能建模方法，该方法利用随机森林预测。基于密集 NS-3 模拟构建的数据集的评估表明，我们可以根据各种特征实现对 incast 和大象性能指标的准确预测。借助这种性能建模功能，智能缓冲方案或流量优化算法可以预测并有效优化系统参数调整，以在数据中心持续实现最佳性能。
Traffic optimization and smart buffering are fundamental to achieve both great application performance and resource efficiency in data centers with heterogeneous workloads, including incast and elephant traffics. However, general performance models providing insights on how various factors affect traffic performance metrics needed by these management functions are missing. For the special case of incast, the existing models are analytical ones, either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, this paper proposes an SDN-enabled machine-learning-based performance modeling approach in data center networks that leverages random forest predictions. Evaluations based on datasets constructed through intensive NS-3 simulations show that we can achieve accurate predictions of incast and elephant performance metrics based on various features. With this performance modeling capability, smart buffering schemes or traffic optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance continuously in data centers.