A maximally robustness embedding algorithm in virtual data centers with multi-attribute node ranking based on TOPSIS - Shooshtarian, L.; Safaei, F. (The Journal of supercomputing, 2019)
The virtualization of the data center network is one of the technologies that enable the performance guarantee and more flexibility and improve the utilization of infrastructure resources in cloud computing. One of the key issues in the management of virtual data center (VDC) is VDC embedding, which deals with the efficient mapping of required virtual network resources from the shared resources of the infrastructure provider (InP). In this paper, we propose a new VDC embedding algorithm that is different from previous works in many aspects. First, the provision of robustness for data center infrastructure is one of the critical requirements of cloud technology; however, this challenge has not been considered in the related literature. In order to analyze and evaluate the robustness of the infrastructure network, the classical and spectral graph robustness metrics are employed. Second, in order to avoid imbalance mapping and increase the efficiency of infrastructure resources, besides the resource dynamic capacity, four node attributes are exploited to compute the nodes mapping potential. The TOPSIS technique for nodes ranking has been used to increase the compatibility with the ideal solution. Third, unlike previous works in which the mapping phases of nodes and links are getting used to being separated, in the proposed algorithm, the virtual network is mapped to a physical network in a single step. Fourth, we also consider resources for network nodes (switches or routers). For these purposes, a multi-objective mathematical optimization problem is extracted with two goals of maximizing infrastructure network robustness and minimizing the long-term average cost-to-revenue ratio mapping for InPs. Finally, a new single-stage (non-dominated sorting-based genetic algorithm) NSGAII-based online VDCE algorithm is presented, where node mapping is TOP-MANR based and edge mapping is based on the shortest path. The fat-tree topology is considered for the substrate and virtual networks, and these two networks are modeled as a weighted undirected graph.