Service composition (SC) generates various composite applications quickly by using a novel service interaction model. Before composing services together, the most important thing is to find optimal candidate service instances compliant with non-functional requirements. Particle swarm optimization (PSO) is known as an effective and efficient algorithm, which is widely used in this process. However, the premature convergence and diversity loss of PSO always results in suboptimal solutions. In this paper, we propose an accurate sub-swarms particle swarm optimization (ASPSO) algorithm by adopting parallel and serial niching techniques. The ASPSO algorithm locates optimal solutions by using sub-swarms searching grid cells in which the density of feasible solutions is high. Simulation results demonstrate that the proposed algorithm improves the accuracy of the standard PSO algorithm in searching the optimal solution of service selection problem.

网络虚拟化技术可以在共用的底层网络基础设施上同时构建多个彼此隔离的虚拟网络,为用户提供差异化服务,从而解决现有因特网的僵化问题.然而,一个重要的挑战是,如何在共用的基础设施中高效地映射多个具有不同拓扑的虚拟网络,即虚拟网络的嵌套映射问题.主要根据基础设施的构成方式对现有的虚拟网络映射算法进行了综述.首先,阐述了网络虚拟化的概念、特点以及相应的虚拟网络映射模型;其次,按照基础设施的构成方式、问题空间完整性、映射阶段数等方面梳理了嵌套映射算法的最新研究进展;最后,对虚拟网络映射算法在公平性、扩展性、高利用率、信任度等未来可能的发展方向进行了展望.

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops.

Service composition is an efficient way to implement a service of complex business process. In service composition, service selection considering multiple QoS objectives is a challenge. Existing methods mainly utilize fitness function or constraint technique to convert multiple objectives service composition problems to single objective ones. These methods take effect with priori knowledge of problem's solution space. Besides, only one solution can be obtained in each execution of one existing method. Users can hardly acquire evenly distributed solutions with acceptable computation cost. In this paper, we propose a lightweight particle swarm optimization service selection algorithm using approximate distance and external archive mechanisms. Simulation results demonstrate that the proposed algorithm is more effective and efficient than the compared algorithm for multi-objective service composition problems.

The most important thing of service composition (SC) is to select optimal candidate service instances compliant with non-functional requirements (e.g. QoS and load balance constraints). Particle swarm optimization (PSO) is known as an effective and efficient algorithm, which is widely used in this process. However, the premature convergence and diversity loss of PSO may result in suboptimal solutions. In this paper, we propose an accurate sub-swarms particle swarm optimization (ASPSO) algorithm which locates optimal solutions by using sub-swarms searching grid cells in which the density of feasible solutions is high. Simulation results demonstrate that the proposed algorithm improves the accuracy of the standard PSO algorithm in service composition.

Pattern matching for intrusion feature strings is an important basis for detecting invasion, thus the efficiency of pattern matching is a key factor influencing the performance of intrusion detection. Based on the discussion of the classic BM (Boyer-Moore), BMH (Boyer-Moor-Horspool) and Sunday algorithms for pattern matching, an improved algorithm Sunday-C is proposed. By adding an extra skip before a match, Sunday-C produces a bigger skip distance for reducing the number of match loops and increasing the match the efficiency. Theoretical analysis and experimental test of this paper compares the classic algorithm and improved algorithms for matching performance. The result shows that improved algorithm can save matching time. When applied to the intrusion detection, this algorithm will enhance the detection efficiency.

Fountain codes are becoming increasingly important for data transferring over dedicated high-speed long-distance network. However, the encoding and decoding complexity of traditional fountain codes such as LT and Raptor codes are still high. In this paper, a new fountain codes named LRF (Loss Rate Based Fountain) codes for data transfer is proposed. In order to improve the performance of encoding and decoding efficiency and decrease the number of redundant encoding symbols, an innovative degree distribution instead of robust soliton degree distribution in LT (Luby Transfer) codes is proposed. In LRF codes, the degree of encoding symbol is decided by loss rate property, and the window size is extended dynamic. Simulations result using LRF codes show that the proposed method has better performance in term of encoding ratio, degree ratio, encoding and decoding efficiency with respect to LT and Raptor codes.

Network virtualization provides a powerful tool to allow multiple networks, each customized to a specific purpose, to run on a shared substrate. However, a big challenge is how to map multiple virtual networks onto specific nodes and links in the shared substrate network, known as virtual network embedding problem. Previous works in virtual network embedding can be decomposed to two classes: two-stage virtual network embedding and one-stage virtual network embedding. In this paper, by pruning the topology of virtual network using k-core decomposition, a hybrid virtual network embedding algorithm is proposed to leverage the respective advantage of the two kinds of algorithm simultaneously in the mapping process. In addition, a time-oriented scheduling policy is introduced to improve the mapping performance. We conduct extensive simulations and the results show that the proposed algorithm obtains more revenue in the long-term run.

Aligning different ontologies from similar (or same) domains is an active field of current research. There are various solutions which process and analyze lexical, structural or semantic information to align ontologies. However, there are few solutions that focus on interpreting the concepts that entities are presented with and using them in relation to the semantics implied in an ontology. In this paper, the prototype (OACLAI) is presented to tackle this by combining lexical analysis with consequences from reasoners which reflect the semantics implied in an ontology. We evaluate OACLAI over the four real ontologies and compare it against the seven solutions. The experiments show that the accuracy of OACLAI is higher than those of others on average.

For exploiting semantics implied in constructors and axioms in an ontology,a structural subsumption reasoning algorithm was extended to match ontologies.The implied semantics was first enabled to be read-off easily by rephrasing entities in an ontology into normal forms; then the syntactical structure of the normal forms were compared to infer correspondences between entities,one from each ontology.The prototype was evaluated over four in use ontologies and compared against twelve methods.The experiments show that its performance is higher than other solutions on average.

The paper presents a lexical analysis algorithm based on combining senses of words for computing subsumption relations between entities in ontology matching.It firstly finds the suitable sense of each word and extends it;then formally defines the representation of an entity notion based on semantic elements;finally,infers subsumption relations between all possible pair of entities,one from each of two ontologies.The experiments,over four real in use ontologies,show that the algorithm helps to increase the recall of the system.

Service oriented architecture (SOA) lends itself to model the application components to coarse-grained services in such a way that the composition of different services could be feasible. Service composition fulfills numerous service requirements by constructing composite applications with various services. As it is the case in many real-world applications, different users have diverse QoS demands issuing for composite applications. In this paper, we present a service composition framework for a typical service overlay network (SON) considering both multiple QoS constraints and load balancing factors. Moreover, a service selection algorithm based on niching technique and particle swarm optimization (PSO) is proposed for the service composition problem. It supports optimization problems with multiple constraints and objective functions, whether linear or nonlinear. Simulation results show that the proposed algorithm results in an acceptable level of efficiency regarding the service composition objective under different circumstances.

TBCP(talk burst control protocol) has been defined as the floor control mechanism for PoC(push to talk over cellular) service by OMA(open mobile alliance).It is a centralized controlling mechanism which has several limitations as other central approaches have,such as bottleneck effect,bad scalability,high maintenance cost of the central node,etc.In order to overcome the deficiencies of TBCP,two novel distributed floor control mechanisms were put forward,TBCP/DQ(distributed queue) and TBCP/MQ(mobile queue).After analytical and experimental evaluations,the new ap-proaches serve well for PoC and even all CMA(collaborative multimedia application) services.

Fault localization is a central element in network fault management. This paper takes a weighted bipartite graph as a fault propagation model and presents a heuristic fault localization algorithm based on the idea of incremental coverage, which is resilient to inaccurate fault propagation model and the noisy environment. Furthermore, a sliding window mechanism is proposed to tackle the inaccuracy of this algorithm in the presence of improper time windows. As shown in the simulation study, our scheme achieves higher detection rate and lower false positive rate in the noisy environment as well as in the presence of inaccurate windows, than current fault localization algorithms.

SUMMARYThe network mobility (NEMO) is proposed to support the mobility management when users move as a whole. In IP Multimedia Subsystem (IMS), the individual Quality of Service (QoS) control for NEMO results in excessive signaling cost. On the other hand, current QoS schemes have two drawbacks: unawareness of the heterogeneous wireless environment and inefficient utilization of the reserved bandwidth. To solve these problems, we present a novel heterogeneous bandwidth sharing (HBS) scheme for QoS provision under IMS‐based NEMO (IMS‐NEMO). The HBS scheme selects the most suitable access network for each session and enables the new coming non‐real‐time sessions to share bandwidth with the Variable Bit Rate (VBR) coded media flows. The modeling and simulation results demonstrate that the HBS can satisfy users' QoS requirement and obtain a more efficient use of the scarce wireless bandwidth. Copyright © 2011 John Wiley & Sons, Ltd.

The Session Initiation Protocol (SIP) is an application-layer control protocol standardized by the IETF for creating, modifying and terminating multimedia sessions. With the increasing use of SIP in large deployments, the current SIP design cannot handle overload effectively, which may cause SIP networks to suffer from congestion collapse under heavy offered load. This paper introduces a distributed end-to-end overload control (DEOC) mechanism, which is deployed at the edge servers of SIP networks and is easy to implement. By applying overload control closest to the source of traffic, DEOC can keep high throughput for SIP networks even when the offered load exceeds the capacity of the network. Besides, it responds quickly to the sudden variations of the offered load and achieves good fairness. Theoretic analysis and extensive simulations verify that DEOC is effective in controlling overload of SIP networks.

To reduce the inaccuracy caused by inappropriate time window, we propose two probabilistic fault localization schemes based on the idea of “extending time window.” The global window extension algorithm (GWE) uses a window extension strategy for all candidate faults, while the on-demand window extension algorithm (OWE) uses the extended window only for a small set of faults when necessary. Both algorithms can increase the metric values of actual faults and thus improve the accuracy of fault localization. Simulation results show that both schemes perform better than existing algorithms. Furthermore, OWE performs better than GWE at the cost of a bit more computing time.

This paper proposed a MDL(Minimum Description Length) principle based dynamic community partition algorithm that is named DCI(Dynamic Community Identification).This paper defines evolving relationships between static communities at adjacent time points,so that DCI can not only identify the dynamic communities with different life cycles but also describe their evolving processes accurately.Experiments demonstrate that the DCI algorithm can identify dynamic communities more accurately than the existing algorithms and it's applicable to processing large scale dynamic networks in acceptable time.