Yanni Han

Virtual network embedding by exploiting topological information

Network virtualization provides a powerful way to run multiple heterogeneous virtual networks (VNs) at the same time on a shared substrate network. A major challenge in network virtualization is the efficient virtual network embedding: mapping virtual nodes and virtual edges onto substrate networks. Previous researches have presented several heuristic algorithms, which fail to consider the topology attributes of substrate and virtual networks. However, the topology information affects the performance of the embedding obviously. In this paper, for the first time, we exploit the topology attributes of substrate and virtual networks, introduce network centrality analysis into the virtual network embedding, and propose virtual network embedding algorithms based on closeness centrality. Due to considering the topology information, our study is more reasonable than the existing work in coordinating node and edge embedding. In addition, with the guidance of topology quantitative evaluation, the proposed network embedding approaches largely improve the network utilization efficiency and decrease the embedding complexity. Experimental results demonstrate the usability and feasibility of the proposed approach.

Topology-aware virtual network embedding based on closeness centrality

Network virtualization aims to provide a way to overcome ossification of the Internet. However, making efficient use of substrate resources requires effective techniques for embedding virtual networks: mapping virtual nodes and virtual edges onto substrate networks. Previous research has presented several heuristic algorithms, which fail to consider that the attributes of the substrate topology and virtual networks affect the embedding process. In this paper, for the first time, we introduce complex network centrality analysis into the virtual network embedding, and propose virtual network embedding algorithms based on closeness centrality. Due to considering of the attributes of nodes and edges in the topology, our studies are more reasonable than existing work. In addition, with the guidance of topology quantitative evaluation, the proposed network embedding approach largely improves the network utilization efficiency and decreases the embedding complexity. We also investigate our algorithms on real network topologies (e.g., AT&T, DFN) and random network topologies. Experimental results demonstrate the usability and capability of the proposed approach.

Vulnerability-constrained multiple minimum cost paths for multi-source wireless sensor networks

In wireless sensor networks, one of the primary requirements is that sensor data acquired from the physical world can be interchanged with all interested collaborative entities in a secure, reliable manner. Because of highly unpredictable nature of the environments caused by malicious attacks or potential threats, minimizing transmission cost between source and sink nodes with jointly considering the security of the whole network is a critical issue. This paper considers two optimization problems of deriving the minimum cost paths from multiple source nodes to the sink node under the guaranteed level of the vulnerability. The link or node vulnerability is defined as a metric, which characterizes the degree of link or node sharing among paths. With the defined link vulnerability, the link vulnerability-constrained minimum cost paths problem is first formulated, and two polynomial-time algorithms are developed for deriving the optimal paths. For the node-vulnerability-constrained minimum cost paths problem, we adopt the network conversion and then achieve the optimal solution with previous proposed algorithms. The necessary condition for solution existence, the optimality of the proposed algorithms, and the related properties of tree network are further theoretically analyzed. Extensive simulations show the significant performance improvements achieved by our proposed algorithms.Copyright

A cost-effective scheme supporting adaptive service migration in cloud data center

Cloud computing as an emerging technology promises to provide reliable and available services on demand. However, offering services for mobile requirements without dynamic and adaptive migration may hurt the performance of deployed services. In this paper, we propose MAMOC, a cost-effective approach for selecting the server and migrating services to attain enhanced QoS more economically. The goal of MAMOC is to minimize the total operating cost while guaranteeing the constraints of resource demands, storage capacity, access latency and economies, including selling price and reputation grade. First, we devise an objective optimal model with multi-constraints, describing the relationship among operating cost and the above constraints. Second, a normalized method is adopted to calculate the operating cost for each candidate VM. Then we give a detailed presentation on the online algorithm MAMOC, which determines the optimal server. To evaluate the performance of our proposal, we conducted extensive simulations on three typical network topologies and a realistic data center network. Results show that MAMOC is scalable and robust with the larger scales of requests and VMs in cloud environment. Moreover, MAMOC decreases the competitive ratio by identifying the optimal migration paths, while ensuring the constraints of SLA as satisfying as possible.

A reference model for virtual resource description and discovery in virtual networks

The virtual resource description and provisioning play a key role in virtual resources discovery, selection and binding process. However, there lacks a standard resource description schema for network virtualization. In this paper, we propose a virtual network resource description model, which can give a reference for ISPs (Internet Service Providers) to unify resource management. Furthermore, we extend the WSDL (Web Service Description Language) to specify this model, which is motivated for three reasons. The WSDL supports dynamical update services, which is precisely lacking in the existing network description language. In addition, WSDL is based on XML syntax and is flexible extended for accommodating more properties. Moreover, the resources are essentially services with minimum granularity. Besides the resource definition model and the WSDL-based virtual resource description schema, we also design a virtual resource provisioning framework to confirm the implementation of our proposals. Both theoretical analysis and scenarios demonstration show that the proposed model and framework are effective in dynamic resource discovery and resource composition.

An Efficient Resource Embedding Algorithm in Software Defined Virtualized Data Center

Cloud computing is a promising paradigm for future computing platform. It enables the physical resources(computing, storage, networking, etc) to be provided on-demand. Despite the cloud computing brings many benefits to the IT network structure, it still faces new challenges for allocating heterogeneous resources automatically to services. Software Defined Networking (SDN) is a new concept of the network infrastructure as it decouples the control and data planes.With its characteristic of programmability, its feasible to achieve network virtualization or create a slice of network in the form of virtual data centers(VDCs), which have pushed huge development of cloud computing. The VDCs embedding problem deals with allocating VDC requests to fulfill the requirements of cloud services with minimal cost and high revenue, which is an NPhard problem. In this paper, we propose an algorithm based on Entropy Weighted topological Potential considering multiple types of resources to tackle the problem in SDN-enabled data center network. Extensive simulations show that our proposed algorithm can embed more virtual requests efficiently with higher acceptance ratio and resource revenue over time.

Achieving energy-neutral data transmission by adjusting transmission power for energy-harvesting wireless sensor networks

Recently, benefiting from rapid development of energy harvesting technologies, the research trend of wireless sensor networks has shifted from the battery-powered network to the one that can harvest energy from ambient environments. In such networks, a proper use of harvested energy poses plenty of challenges caused by numerous influence factors and complex application environments. Although numerous works have been based on the energy status of sensor nodes, no work refers to the issue of minimizing the overall data transmission cost by adjusting transmission power of nodes in energy-harvesting wireless sensor networks. In this paper, we consider the optimization problem of deriving the energy-neutral minimum cost paths between the source nodes and the sink node. By introducing the concept of energy-neutral operation, we first propose a polynomial-time optimal algorithm for finding the optimal path from a single source to the sink by adjusting the transmission powers. Based on the work earlier, another polynomial-time algorithm is further proposed for finding the approximated optimal paths from multiple sources to the sink node. Also, we analyze the network capacity and present a near-optimal algorithm based on the Ford-Fulkerson algorithm for approaching the maximum flow in the given network. We have validated our algorithms by various numerical results in terms of path capacity, least energy of nodes, energy ratio, and path cost. Simulation results show that the proposed algorithms achieve significant performance enhancements over existing schemes. Copyright

A Wireless Traffic QoS Optimization Algorithm Based on Fuzzy Measurement

—A cross-layer based QoS optimization algorithm for wireless traffic networking is presented in this paper. In terms of the fuzzy measure theory, we propose a nonlinear wireless traffic networking optimization model based on the Choquet integral. The model can characterize not only the protocol parameters’ significance but also the interdependency among those parameters on the QoS of data transmission by a nonadditive function. The distinct characteristic of the proposed model lies in that the contribution of interaction among the system parameters to the network performance can be evaluated quantitatively by a general nonlinear and non-additive integral. Once the network condition cannot satisfy the user’s QoS requirement, the most significant networking parameters can be adjusted to improve the data transmission performance and further achieve the user’s QoS demand. Finally, simulation results are given to verify the effectiveness and efficiency of the proposed method over the WLAN network.

A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential

Inspired by the theory of physics field, in this paper, we propose a novel backbone network compression algorithm based on topology potential. With consideration of the network connectivity and backbone compression precision, the method is flexible and efficient according to various network characteristics. Meanwhile, we define a metric named compression ratio to evaluate the performance of backbone networks, which provides an optimal extraction granularity based on the contributions of degree number and topology connectivity. We apply our method to the public available Internet AS network and Hep-th network, which are the public datasets in the field of complex network analysis. Furthermore, we compare the obtained results with the metrics of precision ratio and recall ratio. All these results show that our algorithm is superior to the compared methods. Moreover, we investigate the characteristics in terms of degree distribution and self-similarity of the extracted backbone. It is proven that the compressed backbone network has a lot of similarity properties to the original network in terms of power-law exponent.

Towards reliable virtual data center embedding in software defined networking

The software defined networking is attracting significant attention from business and private end-users. With characteristic of virtualization and programmability, Service Providers (SPs) can pay for the resources in the term of virtual data centers (VDCs) to deploy their services without maintaining expertise. Despite advantages the new paradigm brings, offering VDCs as a service still faces a new challenge. It is the VDC embedding problem which is known as NP-hard. In this work we propose VDC embedding methods considering the tradeoff among multiple requirements, such as the reliability, revenue and bandwidth occupation. Our goal is to reduce resource occupation rate, increase the acceptance ratio of VDCs and improve the revenue for infrastructure providers (InPs). To reduce embedding cost and ensure the reliability requirement, our proposals try to group VMs with more traffic to a cluster based on topological potential and modularity and embed the VM clusters to distinct servers near to each other. Extensive simulations show that our proposed methods can embed more VDCs efficiently with higher resource utilization.