The network traffic matrix is a kind of flow-level Internet traffic data and is widely applied to network operation and management. It is a crucial problem to analyze the composition and structure of traffic matrix; some mathematical approaches such as Principal Component Analysis (PCA) were used to handle that problem . In this paper, we first argue that PCA performs poorly for analyzing traffic matrixes polluted by large volume anomalies,  then propose a new composition model of the network traffic matrix. According to our model, structure analysis can be formally defined as decomposing a traffic matrix into low-rank, sparse, and noise sub-matrixes , which is equal to the Robust Principal Component Analysis (RPCA) problem defined in [13]. Based on the Relaxed Principal Component Pursuit (Relaxed PCP) method and the Accelerated Proximal Gradient (APG) algorithm, an iterative algorithm for decomposing a traffic matrix is presented, and our experiment results demonstrate its efficiency and flexibility  . At last, further discussions on the deterministic traffic and the noise trafficare carried out. Our study gives a proper method for traffic matrix structure analysis, which has a defense against the pollution of large volume anomalies.
The network traffic matrix is widely used in network operation and management. It is therefore of crucial importance to analyze the components and the structure of the network traffic matrix, for which several mathematical approaches such as Principal Component Analysis (PCA) were proposed . In this paper, we first argue that PCA performs poorly for analyzing traffic matrix that is polluted by large volume anomalies, and then propose a new decomposition model for the network traffic matrix. According to this model, we carry out the structural analysis by decomposing the network traffic matrix into three sub-matrices, namely, the deterministic traffic, the anomaly traffic and the noise traffic matrix , which is similar to the Robust Principal Component Analysis (RPCA) problem previously studied in [13]. Based on the Relaxed Principal Component Pursuit (Relaxed PCP) method and the Accelerated Proximal Gradient (APG) algorithm, we present an iterative approach for decomposing a traffic matrix , and demonstrate its efficiency and flexibility by experimental results. Finally, we further discuss several features of the deterministic and noise traffic . Our study develops a novel method for the problem of structural analysis of the traffic matrix, which is robust against pollution of large volume anomalies.
R
a kind of flow-level Internet traffic data and is widely applied to
widely used in
R
a crucial problem
therefore of crucial importance
R
composition and structure of traffic matrix; some
components and the structure of the network traffic matrix, for which several
R
used to handle that problem
proposed
R
matrixes
matrix that is
A
and
R
composition model of
decomposition model for
R
our model, structure analysis can be formally defined as decomposing a
this model, we carry out the structural analysis by decomposing the network
R
low-rank, sparse, and noise sub-matrixes
three sub-matrices, namely, the deterministic traffic, the anomaly traffic and the noise traffic matrix
R
equal
similar
R
defined
previously studied
R
an iterative algorithm
we present an iterative approach
R
is presented, and our experiment results
, and
A
by experimental results. Finally, we further discuss several features of the deterministic and noise traffic
D
At last, further discussions on the deterministic traffic and the noise trafficare carried out.
R
gives a proper method for traffic matrix structure analysis, which has a defense against the
develops a novel method for the problem of structural analysis of the traffic matrix, which is robust against