Greening Internet is an important issue now, which studies the way to reduce the increas- ing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. W...Greening Internet is an important issue now, which studies the way to reduce the increas- ing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. We formulate the model by traffic engineering to achieve link rate a- daption, and also predict traffic matrices to pre- serve network stability. However, we realize that there is a tradeoff between network performance and energy efficiency, which is an obvious issue as Internet grows larger and larger. An essential cause is the huge traffic, and thus we try to fred its so- lution from a novel architecture called Named Data Networking （NDN） which tent in edge routers and can flexibly cache con- decrease the backbone traffic. We combine our methods with NDN, and finally improve both the network performance and the energy efficiency. Our work shows that it is effective, necessary and feasible to consider green- ing idea in the design of future Internet.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
Traffic matrix is an abstract representation of the traffic volume flowing between sets of source and destination pairs.It is a key input parameter of network operations management,planning,provisioning and traffic en...Traffic matrix is an abstract representation of the traffic volume flowing between sets of source and destination pairs.It is a key input parameter of network operations management,planning,provisioning and traffic engineering.Traffic matrix is also important in the context of OpenFlow-based networks.Because even good measurement systems can suffer from errors and data collection systems can fail,missing values are common.Existing matrix completion methods do not consider traffic exhibit characteristics and only provide a finite precision.To address this problem,this paper proposes a novel approach based on compressive sensing and traffic self-similarity to reconstruct the missing traffic flow data.Firstly,we analyze the realworld traffic matrix,which all exhibit lowrank structure,temporal smoothness feature and spatial self-similarity.Then,we propose Self-Similarity and Temporal Compressive Sensing(SSTCS) algorithm to reconstruct the missing traffic data.The extensive experiments with the real-world traffic matrix show that our proposed SSTCS can significantly reduce data reconstruction errors and achieve satisfactory accuracy comparing with the existing solutions.Typically SSTCS can successfully reconstruct the traffic matrix with less than 32%errors when as much as98%of the data is missing.展开更多
基金This work was supported by the National Key Basic Re- search Program of China under Grant No. 2011 CB302702 the National Natural Science Foundation of China under Grants No. 61132001, No. 61120106008, No. 61070187, No. 60970133, No. 61003225 the Beijing Nova Program.
文摘Greening Internet is an important issue now, which studies the way to reduce the increas- ing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. We formulate the model by traffic engineering to achieve link rate a- daption, and also predict traffic matrices to pre- serve network stability. However, we realize that there is a tradeoff between network performance and energy efficiency, which is an obvious issue as Internet grows larger and larger. An essential cause is the huge traffic, and thus we try to fred its so- lution from a novel architecture called Named Data Networking （NDN） which tent in edge routers and can flexibly cache con- decrease the backbone traffic. We combine our methods with NDN, and finally improve both the network performance and the energy efficiency. Our work shows that it is effective, necessary and feasible to consider green- ing idea in the design of future Internet.
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.
基金This work is supported by the Prospcctive Research Project on Future Networks of Jiangsu Future Networks Innovation Institute under Grant No.BY2013095-1-05, the National Ba- sic Research Program of China （973） under Grant No. 2012CB315805 and the National Natural Science Foundation of China under Grants No. 61173167.
文摘Traffic matrix is an abstract representation of the traffic volume flowing between sets of source and destination pairs.It is a key input parameter of network operations management,planning,provisioning and traffic engineering.Traffic matrix is also important in the context of OpenFlow-based networks.Because even good measurement systems can suffer from errors and data collection systems can fail,missing values are common.Existing matrix completion methods do not consider traffic exhibit characteristics and only provide a finite precision.To address this problem,this paper proposes a novel approach based on compressive sensing and traffic self-similarity to reconstruct the missing traffic flow data.Firstly,we analyze the realworld traffic matrix,which all exhibit lowrank structure,temporal smoothness feature and spatial self-similarity.Then,we propose Self-Similarity and Temporal Compressive Sensing(SSTCS) algorithm to reconstruct the missing traffic data.The extensive experiments with the real-world traffic matrix show that our proposed SSTCS can significantly reduce data reconstruction errors and achieve satisfactory accuracy comparing with the existing solutions.Typically SSTCS can successfully reconstruct the traffic matrix with less than 32%errors when as much as98%of the data is missing.
