In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and ...In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.展开更多
With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify hea...With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.展开更多
The periodic cell stream is a very important member among the input traffic sources in ATM networks. In this paper, a finite-buffered ATM multiplexer with traffic sources composed of a periodic cell stream, multiple i...The periodic cell stream is a very important member among the input traffic sources in ATM networks. In this paper, a finite-buffered ATM multiplexer with traffic sources composed of a periodic cell stream, multiple i.i.d Bernoulli cell streams and bursty two-state Markov Modulated Bernoulli Process (MMBP) cell streams is exactly analyzed. The probability mass function of queuing delay, the autocorrelation and power spectrum of delay jitter for this periodic cell stream are derived. The analysis is used to expose the behavior of delay jitter for a periodic cell stream through an ATM multiplexer in a bursty traffic environment. The simulation results indicate that the analytical results are accurate.展开更多
The growing P2P streaming traffic brings a variety of problems and challenges to ISP networks and service providers.A P2P streaming traffic classification method based on sampling technology is presented in this paper...The growing P2P streaming traffic brings a variety of problems and challenges to ISP networks and service providers.A P2P streaming traffic classification method based on sampling technology is presented in this paper.By analyzing traffic statistical features and network behavior of P2P streaming,a group of flow characteristics were found,which can make P2P streaming more recognizable among other applications.Attributes from Netflow and those proposed by us are compared in terms of classification accuracy,and so are the results of different sampling rates.It is proved that the unified classification model with the proposed attributes can identify P2P streaming quickly and efficiently in the online system.Even with 1:50 sampling rate,the recognition accuracy can be higher than 94%.Moreover,we have evaluated the CPU resources,storage capacity and time consumption before and after the sampling,it is shown that the classification model after the sampling can significantly reduce the resource requirements with the same recognition accuracy.展开更多
While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore,...While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.展开更多
基金The US National Science Foundation (No.BCS-0527508)
文摘In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.
基金This study is supported by National key research and development program(2016YFB0801200).
文摘With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.
文摘The periodic cell stream is a very important member among the input traffic sources in ATM networks. In this paper, a finite-buffered ATM multiplexer with traffic sources composed of a periodic cell stream, multiple i.i.d Bernoulli cell streams and bursty two-state Markov Modulated Bernoulli Process (MMBP) cell streams is exactly analyzed. The probability mass function of queuing delay, the autocorrelation and power spectrum of delay jitter for this periodic cell stream are derived. The analysis is used to expose the behavior of delay jitter for a periodic cell stream through an ATM multiplexer in a bursty traffic environment. The simulation results indicate that the analytical results are accurate.
基金supported by State Key Program of National Natural Science Foundation of China under Grant No.61072061111 Project of China under Grant No.B08004the Fundamental Research Funds for the Central Universities under Grant No.2009RC0122
文摘The growing P2P streaming traffic brings a variety of problems and challenges to ISP networks and service providers.A P2P streaming traffic classification method based on sampling technology is presented in this paper.By analyzing traffic statistical features and network behavior of P2P streaming,a group of flow characteristics were found,which can make P2P streaming more recognizable among other applications.Attributes from Netflow and those proposed by us are compared in terms of classification accuracy,and so are the results of different sampling rates.It is proved that the unified classification model with the proposed attributes can identify P2P streaming quickly and efficiently in the online system.Even with 1:50 sampling rate,the recognition accuracy can be higher than 94%.Moreover,we have evaluated the CPU resources,storage capacity and time consumption before and after the sampling,it is shown that the classification model after the sampling can significantly reduce the resource requirements with the same recognition accuracy.
文摘While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.
