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QOS aware applications have propelled the development of two complementary technologies, Multicasting and Differentiated Services. To provide the required QOS on the Internet, either the bandwidth needs to be increased (Multicasting) or limited bandwidth prioritized among users (DiffServ). Although, the bandwidth on the Internet is continually increasing, the backbone is still insufficient to support QOS without resource allocations. Hence, there is a need to map multicasting in a DiffServ Environment to conserve network bandwidth and to provision this bandwidth in an appropriate fashion. In this regard, two issues have to be addressed. One, the key difference between multicast and DiffServe routing is the structure of the multicast tree. This tree is maintained in multicast aware routers whereas in DiffServe, the core routers maintain no state information regarding the flows. Second, the task of restructuring the multicast tree when members join/leave. Currently, the first issue is addressed by embedding the multicast information within the packet itself as an additional header field. In this paper, we propose a neural network based heuristic approach to address the second problem of routing in a dynamic DiffServe Multicast environment.
Many dynamic multicast routing algorithms have been proposed. The greedy algorithm creates a near optimal tree when a node is added but requires many query/reply messages. The PSPT algorithm cannot construct a cost optimal tree. The VTDM algorithm requires the estimated number of nodes that will join and is not flexible. The problem of building an optimal tree to satisfy QOS requirements at minimum cost and taking minimum network resources is NP- complete and none of the above solutions give an optimal solution.
We have modeled this combinatorial optimization as a nonlinear programming problem and trained an artificial neural network to solve the problem. The problem is tractable only when the QOS parameters are combined into DiffServe classes because of the flows are short-liv
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