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These services require a packet to be classified so that a set of rules can be applied to such network header information as the destination address, flow identifier, port number, or layer-4 protypel type.

Finally, we reconstruct all origin-destination flows from the observed results by solving the reconstruction model.

An important and amazing find of our work is that the sizes of origin-destination flows obey the power laws.

In terms of the known traffic information, we reconstruct all origin-destination flows via compressive sensing method.

Taking advantage of this characteristic, we propose a novel approach to select partial origin-destination flows which are to be measured directly.

In this paper, we exploit the characteristics of origin-destination flows and thus grasp the properties of end-to-end network traffic.

The routing table E ( i ) of node i ∈ N consists of K entries representing different paths passing through node i and relative to active source-destination flows.

In detail, here, we combine the power laws and the constraints of compressive sensing (namely restricted isometry property) together to build measurement matrix and pick up the partial origin-destination flows.

Figure 9 Secondary link activations for source-destination flow between SU 11 and SU 15.

Just for the sake of simplicity but without any loss of generality, this section will make reference to a single source-destination flow (S,D).

Moreover DXFP is a multipath routing protocol as it maintains multiple paths for the same source-destination flow and chooses to forward the packets of the given flow through the path of minimal cost.

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