Revenue Of Infrastructure Providers Research Articles

Network Virtualization with Energy Efficiency Optimization for Wireless Heterogeneous Networks

In wireless network virtualization, guaranteeing service contracts with different mobile virtual network operators (MVNOs) and optimizing energy efficiency are crucial for the success of the virtualization scheme deployed by an infrastructure provider (InP). In this paper, a novel design framework is proposed for resource allocation in an OFDMA virtualized wireless network (VWN). Treating the virtual resources for a VWN as commodities, the InP wants to maximize its revenue by leasing the infrastructure and resources to the MVNOs while meeting certain contract agreements. Moreover, MVNOs want to serve their users at the best performance and pay the minimum cost to the InP. A Lyapunov based online algorithm is proposed to solve the InP's long-term optimization problem. The short-term optimization problem of the InP is considered as a combinatorial nonconvex problem. A multiple time-scale framework is proposed to solve the optimization problem of the InP, which decomposes the pricing decision, base station assignment, and resource allocation into different time-scale algorithms to achieve the design objectives. First, a distributed matching based algorithm is proposed to solve the base station assignment problem. Second, we propose a successive convex approximation approach to solve the joint subchannel assignment and energy efficiency problem. Finally, we propose a branch and bound based algorithm to optimally solve the price decision problem. Simulation results show the trade-off between energy efficiency, InP's revenue, and the isolation provisioning.

Fiber-Wireless Network Virtual Resource Embedding Method Based on Load Balancing and Priority

Network virtualization is becoming one of the most promising ways to solve resource solidification problem of fiber-wireless access networks. To fully utilize substrate resources, a virtual resource embedding method including three sub-mechanisms is presented in this paper. The first one is the priority-based virtual network request (VNR) selecting mechanism, which sorts VNRs and preferentially selects higher priority VNRs for embedding. Then a dynamic embedding mechanism based on load balancing is presented to increase the acceptance rate of VNRs. Second, when embedding is saturated, an embedding update mechanism is proposed to deal with the arrival of new VNRs. It promotes the acceptance rate of high-priority VNRs and the revenue of the infrastructure provider (InP). Finally, a backup resource sharing mechanism is designed to improve reliability, which enables backup resources to be shared and responds to the failures with minimal resources. The simulation results show that the proposed embedding method can balance the resource consumption better than the Greedy Node Embedding and Shortest-path Link Embedding Algorithm, as well as the Greedy Node Embedding and Maximum Residual Bandwidth Link Embedding Algorithm. Besides, by applying our proposed algorithm, higher VNR acceptance rate, InP revenue, and resource utilization are achieved.

A model for virtual network embedding across multiple infrastructure providers using genetic algorithm

Network virtualization is an important aspect in cloud computing, where network is assumed to be consisting of infinite amount of nodes and links. The substrate network (physical network), has limited capacity and so virtual network embedding on the substrate network becomes a problem. Virtual network embedding is a computationally hard problem, considering various constraints on nodes and links. The proposed work applies Genetic Algorithm for the virtual network embedding problem for mapping multiple virtual network requests on infrastructure providers managing multiple substrate networks. Performance evaluation, through simulations, indicates that the proposed model preforms better for the performance metrics such as infrastructure provider revenue, acceptance ratio and node and link utilization in comparison to few other contemporary mapping models.

A unified enhanced particle swarm optimization‐based virtual network embedding algorithm

SUMMARYVirtual network (VN) embedding is a major challenge in network virtualization. In this paper, we aim to increase the acceptance ratio of VNs and the revenue of infrastructure providers by optimizing VN embedding costs. We first establish two models for VN embedding: an integer linear programming model for a substrate network that does not support path splitting and a mixed integer programming model when path splitting is supported. Then we propose 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. In VNE‐UEPSO, the parameters and operations of the particles are well redefined according to the VN embedding context. To reduce the time complexity of the link mapping stage, we use shortest path algorithm for link mapping when path splitting is unsupported and propose greedy k‐shortest paths algorithm for the other case. Furthermore, a large to large and small to small preferred node mapping strategy is proposed to achieve better convergence and load balance of the substrate network. The simulation results show that our algorithm significantly outperforms previous approaches in terms of the VN acceptance ratio and long‐term average revenue. Copyright © 2012 John Wiley & Sons, Ltd.