a c m s i g c o m m ABSTRACTVirtualizing 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.

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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.

SSR markers are desirable markers in analysis of genetic diversity, quantitative trait loci mapping and gene locating. In this study, SSR markers were developed from two genomic libraries enriched for (GA)n and (CA)n of foxtail millet [Setaria italica (L.) P. Beauv.], a crop of historical importance in China. A total of 100 SSR markers among the 193 primer pairs detected polymorphism between two mapping parents of an F(2) population, i.e. "B100" of cultivated S. italica and "A10" of wild S. viridis. Excluding 14 markers with unclear amplifications, and five markers unlinked with any linkage group, a foxtail millet SSR linkage map was constructed by integrating 81 new developed SSR markers with 20 RFLP anchored markers. The 81 SSRs covered nine chromosomes of foxtail millet. The length of the map was 1,654 cM, with an average interval distance between markers of 16.4 cM. The 81 SSR markers were not evenly distributed throughout the nine chromosomes, with Ch.8 harbouring the least (3 markers) and Ch.9 harbouring the most (18 markers). To verify the usefulness of the SSR markers developed, 37 SSR markers were randomly chosen to analyze genetic diversity of 40 foxtail millet accessions. Totally 228 alleles were detected, with an average 6.16 alleles per locus. Polymorphism information content (PIC) value for each locus ranged from 0.413 to 0.847, with an average of 0.697. A positive correlation between PIC and number of alleles and between PIC and number of repeat unit were found [0.802 and 0.429, respectively (P < 0.01)]. UPGMA analysis revealed that the 40 foxtail millet cultivars could be grouped into five clusters in which the landraces' grouping was largely consistent with ecotypes while the breeding varieties from different provinces in China tended to be grouped together.

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Network virtualization has caught the attention of many researchers in recent years. It facilitates the process of creating several virtual networks over a single physical network. Despite this advantage, however, network virtualization suffers from the problem of mapping virtual links and nodes to physical network in most efficient way. This problem is called virtual network embedding ("VNE"). Many researches have been proposed in an attempt to solve this problem, which have many optimization aspects, such as improving embedding strategies in a way that preserves energy, reducing embedding cost and increasing embedding revenue. Moreover, some researchers have extended their algorithms to be more compatible with the distributed clouds instead of a single infrastructure provider ("ISP"). This paper proposes energy aware particle swarm optimization algorithm for distributed clouds. This algorithm aims to partition each virtual network request ("VNR") to sub-graphs, using the Heavy Clique Matching technique ("HCM") to generate a coarsened graph. Each coarsened node in the coarsened graph is assigned to a suitable data center ("DC"). Inside each DC, a modified particle swarm optimization algorithm is initiated to find the near optimal solution for the VNE problem. The proposed algorithm was tested and evaluated against existing algorithms using extensive simulations, which shows that the proposed algorithm outperforms other algorithms.

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