Zhongbao Zhang

Virtual network embedding through topology-aware node ranking

Virtualizing and sharing networked resources have become a growing trend that reshapes the computing and networking architectures. Embedding multiple virtual networks (VNs) on a shared substrate is a challenging problem on cloud computing platforms and large-scale sliceable network testbeds. In this paper we apply the Markov Random Walk (RW) model to rank a network node based on its resource and topological attributes. This novel topology-aware node ranking measure reflects the relative importance of the node. Using node ranking we devise two VN embedding algorithms. The first algorithm maps virtual nodes to substrate nodes according to their ranks, then embeds the virtual links between the mapped nodes by finding shortest paths with unsplittable paths and solving the multi-commodity flow problem with splittable paths. The second algorithm is a backtracking VN embedding algorithm based on breadth-first search, which embeds the virtual nodes and links during the same stage using node ranks. Extensive simulation experiments show 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 compared to the existing embedding algorithms.

Virtual network embedding through topology awareness and optimization

Embedding a sequence of virtual networks (VNs) into a given physical network substrate to accommodate as many VN requests as possible is known to be NP-hard. This paper presents a new approach to studying this problem. In particular, we devise a topology-aware measure on node resources based on random walks and use it to rank a nodes resources and topological attributes. We then devise a greedy algorithm that matches nodes in the VN to nodes in the substrate network according to node ranks. In most situations there exist multiple embedding solutions, and so we want to find the best embedding that increases the possibility of accepting future VN requests and optimizes the revenue for the provider of the substrate network. We present an integer linear programming formulation for this optimization problem when path splitting is not allowed. We then devise a fast-convergent discrete Particle Swarm Optimization algorithm to approximate this problem. Extensive simulation results show that our algorithms produce near optimal solutions and significantly outperform existing algorithms in terms of the ratio of the long-term average revenue over the VN request acceptance.

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

SUMMARY Virtual 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

Energy-aware virtual network embedding

Virtual network embedding, which means mapping virtual networks requested by users to a shared substrate network maintained by an Internet service provider, is a key function that network virtualization needs to provide. Prior work on virtual network embedding has primarily focused on maximizing the revenue of the Internet service provider and did not consider the energy cost in accommodating such requests. As energy cost is more than half of the operating cost of the substrate networks, while trying to accommodate more virtual network requests, minimizing energy cost is critical for infrastructure providers. In this paper, we make the first effort toward energy-aware virtual network embedding. We first propose an energy cost model and formulate the energy-aware virtual network embedding problem as an integer linear programming problem. We then propose two efficient energy-aware virtual network embedding algorithms: a heuristic-based algorithm and a particle-swarm-optimization-technique-based algorithm. We implemented our algorithms in C++ and performed side-by-side comparison with prior algorithms. The simulation results show that our algorithms significantly reduce the energy cost by up to 50% over the existing algorithm for accommodating the same sequence of virtual network requests.

Energy-aware virtual network embedding through consolidation

The rising cost of electricity has forced business organizations to find new ways to cut energy spending. This paper studies how to reduce energy consumption in virtual network embedding, which embeds virtual networks (VNs) requested by users to a shared substrate network (SN) run by an infrastructure provider (InP). Previous research has primarily focused on finding embedding methods to increase revenues by accommodating more VN requests in a fixed SN, with little attention to reducing the energy cost. To fill this gap we formulate an energy consumption model and devise an efficient energy-aware VN embedding algorithm using a consolidation technique. Through preliminary simulations we show that our algorithm can significantly reduce energy consumption by up to 30% over the existing energy-oblivious algorithm, while obtaining attractive revenues for the InPs.

Multiple bulk data transfers scheduling among datacenters

Bulk data migration between datacenters is often a critical step in deploying new services, improving reliability under failures, or implementing various cost reduction strategies for cloud companies. These bulk amounts of transferring data consume massive bandwidth, and further cause severe network congestion. Leveraging the temporal and spacial characteristics of inter-datacenter bulk data traffic, in this paper, we investigate the Multiple Bulk Data Transfers Scheduling (MBDTS) problem to reduce the network congestion. Temporally, we apply the store-and-forward transfer mode to reduce the peak traffic load on the link. Spatially, we propose to lexicographically minimize the congestion of all links among datacenters. To solve the MBDTS problem, we first model it as an optimization problem, and then propose a novel Elastic Time-Expanded Network technique to represent the time-varying network status as a static one with a reasonable expansion cost. Using this transformation, we reformulate the problem as a Linear Programming (LP) model, and obtain the optimal solution through iteratively solving the LP model. We have conducted extensive simulations on a real network topology. The results prove that our algorithm can significantly reduce the network congestion as well as balance the entire network traffic with practical computational costs.

Adaptive multi-objective artificial immune system based virtual network embedding

In network virtualization, there are two decoupled roles involved: (i) infrastructure providers who manage the substrate network, and (ii) service providers who request virtual networks to the infrastructure providers. Embedding virtual networks to a shared substrate network, which is termed as virtual network embedding problem, is widely believed as one of the most significant challenges in such context. For this problem, prior work primarily focuses on either (i) maximizing the revenues by accommodating more virtual network requests or (ii) minimizing the energy consumption by consolidating the virtual networks into minimum number of substrate nodes. In this paper, we aim at achieving these two goals simultaneously. We first formulate the virtual network embedding problem into a multi-objective integer linear programming. We then design an artificial immune system based algorithm to solve this programming. In this algorithm, (i) we design a discrete approach to encode the virtual node mapping solution as an antibody; (ii) to initialize the antibodies, we design two adaptive revenue and energy aware strategies for the node and link mapping, respectively, to strike a balance between revenue and energy costs; (iii) we design corresponding customized strategies in the cloning, crossover and mutation process of artificial immune system in virtual network embedding context; (iv) for the generated antibodies, we leverage the Pareto optimality for evaluating their quality. Through extensive simulations, we show that our algorithm outperforms the state-of-the-art algorithms in terms of the revenue and the energy consumption.

Minimizing electricity cost in geographical virtual network embedding

In light of rapid increase of electricity cost, many business organizations have to find new ways to cut the electricity bill. This paper studies how to reduce the electricity cost in geographical inter-domain virtual network embedding, which embeds virtual networks requested by users to multiple geographically distributed substrate networks run by an infrastructure provider. Previous researches have primarily focused on finding embedding methods to increase revenues by accommodating more virtual network requests, with little attention to reducing the electricity cost. To bridge this gap, we formulate an electricity cost model and design an efficient cost-aware virtual network embedding algorithm by exploiting the location-varying and time-varying diversities of the electricity price and optimizing the energy consumption. Through extensive simulations, we show that our algorithm can significantly reduce the electricity cost by up to 21% over the existing cost-oblivious algorithm, while maintaining nearly the same revenues for the infrastructure provider.

Optimal routing and bandwidth allocation for multiple inter-datacenter bulk data transfers

Bulk data transfers account for a large portion of inter-datacenter traffic, such as backups, propagation of bulky updates and migration of data. These bulk data transfers not only consume massive inter-datacenter bandwidth, but also increase the transmission cost of datacenters. To solve this problem, we first employ the max-min fairness to the design of optimal multiple bulk data transfers scheduling algorithm, which leverages the delay tolerance nature of bulk data and reuses dynamic leftover bandwidth to complete multiple bulk data transfers. Then we apply time-expanded technique to transform the problem under a dynamic network into a static network multi-flow problem, and solve it simultaneously from both routing assignment and bandwidth allocation through iterative linear programming approach. Extensive simulations are conducted on a real datacenter topology to demonstrate that our solutions can: 1) improve the network resource utilization; 2) minimize the average bulk data transfer completion time.

Energy aware virtual network embedding with dynamic demands

In network virtualization, how to efficiently embed virtual networks with both node and link demands into a shared physical network, namely virtual network embedding, has attracted significant attention. Most of prior studies on this problem have the following two limitations: i) they assumed that the virtual network demands are constant values, which does not hold in real-world network since such demands may vary a lot over time; ii) their primary goal was to generate more revenues for the physical network, with no consideration of the energy cost, which has become a critical issue for the physical network. In this paper, we bridge the gap and study the energy aware virtual network embedding with dynamic demands. Specifically, we first model the dynamics of virtual network demands as a combination of following Gaussian distribution and exhibiting daily diurnal pattern. We then design an efficient heuristic algorithm by leveraging the dynamic characteristic of virtual network demands to minimize the energy consumption while keeping high revenue for the physical network. We implemented our algorithm in C++ and performed side-by-side comparison with prior algorithm. Extensive simulations show that our algorithm can significantly reduce the energy cost by up to 16% over the state-of-the-art algorithm, while maintaining near the same revenue.