Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of servi...Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of service(QoS)requirements.In practice,traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource management.To address the issues,we propose to conduct traffic classification with two newly defined QoSaware features,i.e.,inter-APP similarity and intraAPP diversity.The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications.The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application.The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTMAE).The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS classes.Real-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification approach.The evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.展开更多
Environment friendly ferroelectric relaxor Ba(Zr_(0.2)Ti_(0.8))O_(3)thin fims with the addition of 2%Mn dopant were grown on(001)MgO substrates by pulsed laser deposition.Microstructure studies with X-ray di®ract...Environment friendly ferroelectric relaxor Ba(Zr_(0.2)Ti_(0.8))O_(3)thin fims with the addition of 2%Mn dopant were grown on(001)MgO substrates by pulsed laser deposition.Microstructure studies with X-ray di®raction and transmission electron microscopy reveal that the as-grown Ba(Zr_(0.2)Ti_(0.8))O_(3) thin films are c-axis oriented with an atomic sharp interface.The films have good single crystallinity and good epitaxial quality.The interface relationship was determined to be[100]Mn.BZT//[100]MgO and(001)Mn.BZT//(001)MgO.Nanoscale order/disorder relaxor structures were found with nano-columnar structures.The microwave dielectric measurements(15-18GHz)indicate that the¯lms have excellent dielectric properties with large dielectric constant value,high tunability,and low dielectric loss,promising the development of room temperature tunable microwave elements.展开更多
文摘Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of service(QoS)requirements.In practice,traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource management.To address the issues,we propose to conduct traffic classification with two newly defined QoSaware features,i.e.,inter-APP similarity and intraAPP diversity.The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications.The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application.The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTMAE).The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS classes.Real-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification approach.The evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.
基金supported by the National Science Foundation under NSF-NIRT-0709293 and NSF-DMR-0934218the State of Texas through the ARP Program under 003656-0103-2007the Texas Center for Superconductivity at the University of Houston.
文摘Environment friendly ferroelectric relaxor Ba(Zr_(0.2)Ti_(0.8))O_(3)thin fims with the addition of 2%Mn dopant were grown on(001)MgO substrates by pulsed laser deposition.Microstructure studies with X-ray di®raction and transmission electron microscopy reveal that the as-grown Ba(Zr_(0.2)Ti_(0.8))O_(3) thin films are c-axis oriented with an atomic sharp interface.The films have good single crystallinity and good epitaxial quality.The interface relationship was determined to be[100]Mn.BZT//[100]MgO and(001)Mn.BZT//(001)MgO.Nanoscale order/disorder relaxor structures were found with nano-columnar structures.The microwave dielectric measurements(15-18GHz)indicate that the¯lms have excellent dielectric properties with large dielectric constant value,high tunability,and low dielectric loss,promising the development of room temperature tunable microwave elements.
