79 research outputs found
Analysis of OD Flows (Raw Data)
In a recent paper, Structural Analysis of Network Traffic Flows, we analyzed the set of Origin Destination traffic flows from the Sprint-Europe and Abilene backbone networks. This report presents the complete set of results from analyzing data from both networks. The results in this report are specific to the Sprint-1 and Abilene datasets studied in the above paper. The following results are presented here:
1 Rows of Principal Matrix (V) 2
1.1 Sprint-1 Dataset ................................ 2
1.2 Abilene Dataset.................................. 9
2 Set of Eigenflows 14
2.1 Sprint-1 Dataset.................................. 14
2.2 Abilene Dataset................................... 21
3 Classifying Eigenflows 26
3.1 Sprint-1 Dataset.................................. 26
3.2 Abilene Datase.................................... 44Centre National de la Recherche Scientifique (CNRS) France; Sprint Labs; Office of Naval Research (N000140310043); National Science Foundation (ANI-9986397, CCR-0325701
An Improved Traffic Matrix Decomposition Method with Frequency-Domain Regularization
We propose a novel network traffic matrix decomposition method named Stable
Principal Component Pursuit with Frequency-Domain Regularization (SPCP-FDR),
which improves the Stable Principal Component Pursuit (SPCP) method by using a
frequency-domain noise regularization function. An experiment demonstrates the
feasibility of this new decomposition method.Comment: Accepted to IEICE Transactions on Information and System
Adaptive Robust Traffic Engineering in Software Defined Networks
One of the key advantages of Software-Defined Networks (SDN) is the
opportunity to integrate traffic engineering modules able to optimize network
configuration according to traffic. Ideally, network should be dynamically
reconfigured as traffic evolves, so as to achieve remarkable gains in the
efficient use of resources with respect to traditional static approaches.
Unfortunately, reconfigurations cannot be too frequent due to a number of
reasons related to route stability, forwarding rules instantiation, individual
flows dynamics, traffic monitoring overhead, etc.
In this paper, we focus on the fundamental problem of deciding whether, when
and how to reconfigure the network during traffic evolution. We propose a new
approach to cluster relevant points in the multi-dimensional traffic space
taking into account similarities in optimal routing and not only in traffic
values. Moreover, to provide more flexibility to the online decisions on when
applying a reconfiguration, we allow some overlap between clusters that can
guarantee a good-quality routing regardless of the transition instant.
We compare our algorithm with state-of-the-art approaches in realistic
network scenarios. Results show that our method significantly reduces the
number of reconfigurations with a negligible deviation of the network
performance with respect to the continuous update of the network configuration.Comment: 10 pages, 8 figures, submitted to IFIP Networking 201
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