Based on the massive data collected with a passive network monitoring equipment placed in China's backbone,we present a deep insight into the network backbone traffic and evaluate various ways for improving traffi...Based on the massive data collected with a passive network monitoring equipment placed in China's backbone,we present a deep insight into the network backbone traffic and evaluate various ways for improving traffic classifying efficiency in this paper.In particular,the study has scrutinized the network traffic in terms of protocol types and signatures,flow length,and port distribution,from which meaningful and interesting insights on the current Internet of China from the perspective of both the packet and flow levels are derived.We show that the classification efficiency can be greatly improved by using the information of preferred ports of the network applications.Quantitatively,we find two traffic duration thresholds,with which 40% of TCP flows and 70% of UDP flows can be excluded from classification processing while the impact on classification accuracy is trivial,i.e.,the classification accuracy can still reach a high level by saving 85% of the resources.展开更多
A traffic matrix is a necessary parameter fornetwork management functions,and itsupplies a flow-level view of a largescale IP-over-WDM backbone network.This paper studies the problem of traffic matrix estimationand pr...A traffic matrix is a necessary parameter fornetwork management functions,and itsupplies a flow-level view of a largescale IP-over-WDM backbone network.This paper studies the problem of traffic matrix estimationand proposes an exact traffic matrix estimation approach based on network tomography techniques.The traditional network tomography model is extended to make it compatible with compressive sensing constraints.First,a stochastic perturbation is introduced in the traditional network tomography inference model.Then,an algorithm is proposed to achieve additional optical link observations via optical bypass techniques.The obtained optical link observations are used as extensions for the perturbed network tomography model to ensure that the synthetic model can meetcompressive sensing constraints.Finally,the traffic matrix is estimated from the synthetic model by means of a compressive sensing recovery algorithm.展开更多
基金This paper was partially supported by the National Natural Science Foundation of China under Crant No. 61072061111 Project of China under Crant No. B08004 the Fundamental Research Funds for the Central Universities under Grant No. 2009RC0122. References
文摘Based on the massive data collected with a passive network monitoring equipment placed in China's backbone,we present a deep insight into the network backbone traffic and evaluate various ways for improving traffic classifying efficiency in this paper.In particular,the study has scrutinized the network traffic in terms of protocol types and signatures,flow length,and port distribution,from which meaningful and interesting insights on the current Internet of China from the perspective of both the packet and flow levels are derived.We show that the classification efficiency can be greatly improved by using the information of preferred ports of the network applications.Quantitatively,we find two traffic duration thresholds,with which 40% of TCP flows and 70% of UDP flows can be excluded from classification processing while the impact on classification accuracy is trivial,i.e.,the classification accuracy can still reach a high level by saving 85% of the resources.
基金supported in part by the National Natural Science Foundation of China(Nos.61571104,61071124,61501105)the General Project of Scientific Research of the Education Department of Liaoning Province(No.L20150174)+2 种基金the Program for New Century Excellent Talents in University(No.NCET-11-0075)the Fundamental Research Funds for the Central Universities(Nos.N150402003,N120804004,N130504003,N150404018)the State Scholarship Fund(201208210013)
文摘A traffic matrix is a necessary parameter fornetwork management functions,and itsupplies a flow-level view of a largescale IP-over-WDM backbone network.This paper studies the problem of traffic matrix estimationand proposes an exact traffic matrix estimation approach based on network tomography techniques.The traditional network tomography model is extended to make it compatible with compressive sensing constraints.First,a stochastic perturbation is introduced in the traditional network tomography inference model.Then,an algorithm is proposed to achieve additional optical link observations via optical bypass techniques.The obtained optical link observations are used as extensions for the perturbed network tomography model to ensure that the synthetic model can meetcompressive sensing constraints.Finally,the traffic matrix is estimated from the synthetic model by means of a compressive sensing recovery algorithm.
