Static And Dynamic Resource Optimization For Hose-model Virtual Private Network

Virtual Private Network (VPN) is often erected in the public network with adedicated data communication network technology, so that the user can reliablyachieve a lower cost safety transregional interconnection network, the current networkhas been a wide range of applications. MPLS(Multiprotocol Label switch) is atechnology now widely used by telecommunications operators on Backbone network.Using the indicated the destination information label encapsulated in IP packet by theprovider edge routers to achieving exchange but not routing in the network layer.MPLS VPN is a technology of IP-VPN based on MPLS, by using MPLS on the routerand the switcher, simplifying the routing way of core router and combining thetechnology of label switching with the technology of traditional routing. MPLS VPNcan be used to building the wide-bandwidth intranet and extranet to meet the demandsof many kinds of flexible services. It can make the different service systems run overdifferent VPNs and can promise the effective isolation of different services and theservice quality of effective services. With its scalability and reuse advantages ofnetwork resources, Hose model has been a very good application in the VPN.This paper studies the hose model based VPN resource optimization. In view ofthat static hierarchical iterative spanning tree algorithm only considering the reservedbandwidth and can not guaranteeing good QoS performances, we improve thealgorithm to make it using an appropriate selection strategy to consider both minimumreserved bandwidth and minimum total time delay. And we made someimprovements in the algorithm process, such as a more optimal heuristic algorithm toreplace the spanning tree traversal process. The simulation result shows that theimproved algorithm is better than the primal-dual algorithm and the hierarchicaliterative spanning tree algorithm. This paper This paper dose some research relatedtraffic prediction of dynamic hose model based VPN,we analyse measuring modeand signal collection and some network models that have been used to trafficprediction such as short range dependence models, self-similar models and somenew technology. We select BP artificial neural network as traffic prediction model and research on traffic prediction algorithm, improved BP neural network trainingalgorithm. The simulation experiments show that the previous improvements areeffective and this model can efficiently predict the network traffic and dynamic hosemodel using this model shows better performance than static hose model.