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Router power reduction by active performance control realized with support vector machines

机译:通过支持向量机实现的Active Performance Controly实现路由器电源降低

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The machine-learning-based dynamic performance control of routers is proposed to reduce router power consumption. In order to achieve fast adaptability to catch the changing traffic characteristics, we introduce two sequential classification measures; normalization with performance thresholds, and periodic staggered use of learning machine sets in combination with Support Vector Machine (SVM). Numerical experiments using several real Internet traffic data sets elucidate that the router power consumption reduction reaches 50-65%.
机译:提出了基于机器学习的动态性能控制路由器,以减少路由器功耗。 为了实现快速适应性以捕获变化的交通特性,我们介绍了两个顺序分类措施; 使用性能阈值进行归一化,周期性交错使用学习机组与支持向量机(SVM)组合使用。 使用几种真实互联网流量数据集的数值实验阐明了路由器功耗降低达到50-65%。

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