Xiang Cheng

TL;DR: The Markov Random Walk model is applied to rank a network node based on its resource and topological attributes and shows that the topology-aware node rank is a better resource measure and the proposed RW-based algorithms increase the long-term average revenue and acceptance ratio.

TL;DR: This paper devise a topology-aware measure on node resources based on random walks and use it to rank a node's resources and topological attributes and devise a greedy algorithm that matches nodes in the VN to nodes inThe substrate network according to node ranks.

TL;DR: An energy cost model is proposed and two efficient energy-aware virtual network embedding algorithms are proposed: a heuristic-based algorithm and a particle-swarm-optimization-technique- based algorithm.

TL;DR: This paper proposes a unified enhanced particle swarm optimization‐based VN embedding algorithm, called VNE‐UEPSO, to solve these two models irrespective of the support for path splitting, and significantly outperforms previous approaches in terms of the VN acceptance ratio and long‐term average revenue.

TL;DR: An energy consumption model is formulated and an efficient energy-aware VN embedding algorithm is devised using a consolidation technique to significantly reduce energy consumption over the existing energy-oblivious algorithm, while obtaining attractive revenues for the InPs.