Cognitive Virtual Network Topology Reconfiguration Method Based on Traffic Prediction and Link Importance

Abstract

With the increase of network services, how to avoid link congestion and make full use of limited bandwidth resources in network virtualization environment have become the key challenges. In this paper, we introduce the cognitive method into virtual network topology reconfiguration and modify the connectivity to reach the target topology by sensing the traffic. Then, we formulate an optimization problem to maximize the ratio of cumulative saved resources to the square of changed link number (CSR/SCLNR) and determine which virtual link to be added or deleted. Finally, a heuristic algorithm called cognitive virtual network topology reconfiguration method based on traffic prediction and link importance (CVNTRM-TPLI) is proposed to solve this optimization problem. In the CVNTRM-TPLI, the link importance is presented in link deletion as the topological factor to avoid the ping pang effect. Also, a hybrid traffic prediction algorithm based on optimal parameter selection is put forward, where immune optimization is introduced to select the optimal parameters and the virtual network topology can react promptly to the traffic fluctuation without adding or deleting virtual link frequently. Simulation results show that the CVNTRM-TPLI not only has the highest CSR/SCLNR, but also solves the link congestion and makes full use of the limited bandwidth resources.

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

“…If the dynamic changes of network traffic can be predicted in advance, proactive strategies can be taken to adjust SFC to effectively solve the problems such as service interruption caused by network traffic fluctuations. In this paper, the traffic prediction algorithm (HTPA-OPS) in the work of [12] is used to predict a network traffic. The prediction can fit well with the actual network traffic, and it is useful for the SFC dynamic adjustment.…”

“…Step (11)(12)(13)(14)(15)(16)(17)(18)(19) means when the demand for the i-th VNF at t2 is less than 70% of the existing resources and the duration is more than 1s, a VNF scale-down request is generated.…”

“…Constraint (11) means that the residual available CPU resources cpu(nc l ) of the server node nc l cannot be less than the CPU resource demand cpu(vi) of vi, and Nc represents the set of server nodes. Constraint (12) indicates that the residual bandwidth resource of the substrate link es lm should be greater than the link resource demand of SFC, and Es represents the set of substrate links. Constraint (13) means that the residual forwarding capacity of a switch node should be greater than the forwarding resource demand bw(ev ij ).…”

“…One SFC is selected and it contains 3 VNFs and the initial bandwidth demand is 10. The dynamic changes of network traffic within one experimental period of T=150S are predicted using the method proposed in literature [12], and the resource configuration is adjusted to meet the service demand according to the prediction. It is assumed that the change rate of network traffic passing through each VNF is αx, and the changes of network traffic will bring the same proportion change of VNF resource demand, forwarding resource demand and link resource demand.…”

“…A dynamic adjustment method of SFC resource configuration (DA-SFC) is proposed in this paper based on the assumption that the network traffic is dynamic. First, a traffic prediction algorithm in the work of [12] is used to predict the traffic dynamic changes in advance, and duration thresholds are set to generate SFC dynamic adjustment requests. Second, according to an SFC dynamic adjustment request, a proactive SFC dynamic adjustment strategy is generated and VNFs combination and VM reusing are preferred in VNF migration.…”