Research On Network Virtualization Resource Management Mechanism For Future Network

After decades of development, Internet has become a globe information infrastructure with the widest coverage, largest scale and most abundant infor-mation resource in today’s world. With the continuous expansion of network scale, as well as the development of new network communication technolo-gy and Internet business, the "ossification" problems of the traditional Internet have become prominent gradually. It has been unable to meet the sustainable development of future network, mainly in scalability, controllability, manage-ability, measurability, security, mobility and green energy. To this end, the research for future network theory has been actively carried out at home and abroad, involved in the research areas of the future network architecture, net-work virtualization technology, network measurement and prediction.In order to fundamentally solve the problems of the current Internet, this paper studies the virtual resource management for future network from the as-pects of network virtual resource management architecture, virtual resource allocation mechanism and network traffic prediction algorithm. Major research and innovation include:(1) For poor Scalability and autonomy of current network virtual resource management architecture, as well as the lack of measurement&perception, this paper proposes a prediction-based intelligent and distributed virtual resource management architecture (IDP-VRMA). The architecture, using the manage-ment strategy based on multi-layer and multi-domain, realizes the centralized management and distributed control in the local management domain. In each management domain, the management system is responsible for receiving vir-tual network requests, registration, virtual network partition, and contacting with management systems in other management domains to complete cross-domain virtual resource allocation; the intelligent controllers integrated in the substrate network nodes can autonomously perform virtual resource allocation, fault repair, threat handling; the knowledge processing element can monitor the network operation, mine effective knowledge, contributed to perform the corresponding management and control strategies for management system and intelligent controllers. The IDP-VRMA can effectively solve the scalability problem, autonomously implement virtual resource configuration and network failure&threats treatment, have network measurement and perception, greatly improve the validity and reliability of the virtual resource allocation.(2) In the IDP-VRMA architecture, the substrate network resource is allo-cated to many dynamic virtual network requests, affiliated with different SPs. These virtual network requests have different topologies, as well as the needs and constraints of various heterogeneous resource. Hence, there exists complex relationships among multiple VNs or between multiple VNs and InP. That is, multiple VNs simultaneously compete for different resource in different com-ponents of the substrate network. To model the complex relationship, this paper proposes a multidimensional non-cooperative game based virtual resource al-location mechanism. That using the utility function, price function and conges-tion function models the total pay function of each virtual network. Moreover, we prove the existence and uniqueness of the multidimensional Nash equilibri-um in the model. The simulation results show that the multidimensional Nash equilibrium can be achieved in various areas of the game model, the selfish be-haviors of the virtual networks are effectively suppressed, and virtual resource is fairly, reasonably, efficiently allocated.(3) The IDP-VRMA needs a stable and fast network traffic prediction al-gorithm with good prediction performance. For the problem, this paper pro-poses a deterministic echo state network with a loop-feedback reservoir, and applies it to network traffic prediction. The reservoir of the model is con-structed by introducing adjacent feedbacks between adjacent units based on the simplest loop reservoir structure. The absolute of the input weight is the same as the internal weights, and equal to r. Therefore, only one free param-eter is tuned, which greatly simplifies the echo state network. Furthermore, using state update equation and output equation, we prove the good memory capacity of the model. Simulation results show that the model can characterize the self-similarity of network traffic, and have good prediction performance.