面向SOA关键型系统的混合关键度驱动自适应资源管理

针对运行于开放、不确定环境下的SOA架构的多使命复杂关键型系统,提出了一种以混合关键度为驱动的动态分级自适应资源分配算法。它通过构造闭环反馈的分布式控制环,在线动态调整任务Qos,针对不同关键度的任务动态调整资源分配。该方法不但在统计意义上确保了关键型系统的实时性,而且实验证明该方法能有效提高系统在高负载条件下的实际资源利用率和降低关键任务的死线错过率,保证了关键型系统的高可靠性。     

支持组合服务QoS动态优化的服务总线

面向服务的体系结构SOA已经逐渐成为应用软件系统集成的主流技术。服务总线在构建SOA基础架构中起着关键作用,如何根据服务QoS进行动态优化是服务总线中的重要研究课题之一。SOARBus是一个支持QoS动态优化的服务总线,SOARBus实现组合服务请求动态调度算法和QoS感知的组合服务动态调整算法,使其能够根据引擎负载按照负载均衡原则对组合服务请求进行动态调度和以及在组合服务执行期间根据QoS对组合服务进行动态调整。     

基于SOA的实时动态专题地图服务生成方法

针对用户不能在线实时获得个性化专题地图的问题,以ArcGIS的SOA架构为技术支撑,提出一种实时动态专题地图服务生成方法.采用契约优先的Document样式封装服务,利用支持OGC标准的Web服务发布地图数据,使用扩展的二维数据表的叠加操作策略,实现地图数据与第三方渲染数据的融合,以及渲染服务与地图数据发布服务的在线组合.以江苏省农林渔牧总产值为例在线制作系列专题地图,验证了该方法的有效性.     

QoS感知的语义服务组合搜索算法

针对如何在海量Web服务中进行快速的服务自动组合、以满足客户复杂需求的问题,提出一种基于规划图思想的搜索组合算法,该算法在正向扩展阶段除了依据所选取服务的功能与非功能属性进行有效剪枝外,还实时级联更新已驱动服务的QoS信息并有效记录所操作服务的前驱服务,通过前驱索引集合,确保逆向求解阶段的效率并满足了QoS最优的约束。在实验环节证明了注册服务数量与求解效率的线性关系,以及与其他算法相比较所体现出来的性能优势。     

基于服务组合与QoS感知的旅游服务系统

将Web服务组合以及其中的服务质量(QoS)问题应用于旅游服务系统,提出了一个新的QoS代理人模型,该模型将服务端和客户端在QoS问题上结合起来,将服务反馈信息作为了QoS决策时选择服务的重要判断依据之一,实现了基于QoS感知的Web服务组合系统。     

面向服务的SOA物流配送系统

针对目前物流配送系统大都采用多平台的异构系统,导致出现结构不兼容和技术不兼容的混乱情况。探讨了SOA这种面向服务的组件结构的特点及其优势,提出了一种基于这种SOA架构和Web服务相结合的新型物流配送系统,阐明了SOA在现代化物流业建设中的强大生命力和可持续发展的能力。     

基于SOA的Web服务动态组合研究与实现

通过深入分析现有Web服务组合系统优势与不足,将SOA引入到Web服务动态组合领域,提出了一种基于SOA架构和分层协议栈机制构建Web服务动态组合系统的方法,设计并实现了一个基于SOA架构的松散耦合原型系统来实现Web服务组合,可为服务消费者提供一个适当的、开放的、按需配置的及安全的Web服务动态组合环境.     

基于改进遗传算法的QoS感知Web服务组合

传统遗传算法在种群初始化的时候,普遍采用均匀取种法或随机取种法,这些方法生成的种群的平均适应度比较低,难以保证算法的搜索效率。文中提出一种改进的遗传算法用于QoS敏感的Web服务组合,采用两种不同的算法进行服务选择,避免了随机生成初始种群给算法带来的负面影响。并且,该算法将路径模板化以减少服务组合的工作量,用染色体可变长的编码方式来解决组合服务的多路径选择问题。通过仿真实验,与传统的算法相比,所提出的算法在实现服务组合时收敛更快,最优解的适应度更高。     

基于改进磷虾群算法的服务组合优化

面向服务的架构（SOA）下,针对服务组合优化过程中易陷入局部最优、时间开销大的问题,提出一种加入自适应交叉算子和随机扰动算子的改进磷虾群算法PRKH。首先基于服务质量（QoS）建立了服务组合优化模型,并给出不同结构下QoS的计算公式和归一化处理方法。然后在磷虾群（KH）算法的基础上加入自适应的交叉概率和基于实际偏移量的随机扰动,从而在磷虾群的全局搜索能力和局部搜索能力之间达到良好平衡。最后通过仿真,把所提算法与KH算法、粒子群优化（PSO）算法、人工蜂群（ABC）算法和花朵授粉算法（FPA）进行对比,实验结果表明,PRKH算法能够更快找到QoS更优的复合服务。     

QoS感知的云服务优化组合研究

为了更好地解决服务质量感知的云服务优化组合问题,首先对社会认知算法进行了改进,提出了面向离散型优化问题的模仿学习方法以及基于多次变异的观察学习方法。然后使用改进的社会认知算法对服务质量感知的云服务优化组合问题进行了求解。实验结果表明,改进的社会认知算法具有较强的搜索能力和较快的收敛速度,并且具有较强的推广性,可以用来求解其他离散型优化问题。     

Toward effective service composition for real-time SOA-based systems

Many application domains are increasingly leveraging service-oriented architecture (SOA) techniques to facilitate rapid system deployment. Many of these applications are time-critical and, hence, real-time assurance is an essential step in the service composition process. However, there are gaps in existing service composition techniques for real-time systems. First, admission control is an essential technique to assure the time bound for service execution, but most of the service composition techniques for real-time systems do not take admission control into account. A service may be selected for a workflow during the composition phase, but then during the grounding phase, the concrete service may not be able to admit the workload. Thus, the entire composition process may have to be repeated. Second, communication time is an important factor in real-time SOA, but most of the existing works do not consider how to obtain the communication latencies between services during the composition phase. It is clear that maintaining a full table of communication latencies for all pairs of services is infeasible. Obtaining communication latencies between candidate services during the composition phase can also be costly, since many candidate services may not be used for grounding. Thus, some mechanism is needed for estimating the communication latency for composite services. In this paper, we propose a three-phase composition approach to address the above issues. In this approach, we first use a highly efficient but moderately accurate algorithm to eliminate most of the candidate compositions based on estimated communication latencies and assured service response latency. Then, a more accurate timing prediction is performed on a small number of selected compositions in the second phase based on confirmed admission and actual communication latency. In the third phase, specific concrete services are selected for grounding, and admissions are actually performed. The approach is scalable and can effectively achieve service composition for satisfying real-time requirements. Experimental studies show that the three-phase approach does improve the effectiveness and time for service composition in SOA real-time systems. In order to support the new composition approach, it is necessary to effectively specify the needed information. In this paper, we also present the specification model for timing-related information and the extension of OWL-S to support this specification model.     

一种高效的服务组合优化算法

随着功能性属性相同而非功能性属性各异的Web服务的大量涌现,如何在服务组合业务流程中为各个任务选择相应的组件服务以达到组合服务的QoS(quality of service)最大化,并在此基础上满足不同用户的需求,已成为了国内外研究的热点.由于该问题的复杂性(NP-hard),目前存在的大多数方法都并不十分适合需要相对精确、实时决策的Web服务组合系统.因此,本文提出了一种基于凸包构建的组合服务优化算法(CM-HEU)用以解决QoS感知的服务组合优化问题.CM-HEU首先通过对组合服务中的每组任务进行凸包构建,以减少搜索空间.然后通过对初始解向量的多次升级和一次降级操作以达到全局优化的目标.实验表明:相对于现阶段存在的一些主流方法,CM-HEU不仅能得到一个比较理想的结果,并且具有良好的效率.     

A novel heuristic algorithm for QoS-aware end-to-end service composition

Many works have been carried out to find the efficient algorithms for QoS-aware service composition in recent years. Nevertheless, on one hand, some of these works only consider the local QoS attributes in Web services composition; on the other hand, some ideas derived from QoS selection algorithms for network routing are directly applied in service composition without any adaption. A service composition model with end-to-end QoS constraints has been presented in this paper. An improved heuristics HCE based on the observation of characteristic of end-to-end service composition is proposed as a novel solution. Simulation results reveal the better performance of proposed heuristic compared to the other two heuristics, HMCOP and generic CE algorithm.     

An adaptive algorithm for QoS-aware service composition in grid environments

Service composition enables users to realize their complex needs as a single request and it has been recognized as a flexible way for resource sharing and application integration since the appearance of Service-Oriented Architecture (SOA). For each of the needed individual services there may be many candidate services available presented by different vendors and with different functional and non-functional properties such as Quality of Service (QoS). Approaches are needed to select candidate services with various QoS levels according to user’s performance requirements meanwhile adapt to dynamic churn in grid environments. This paper mainly focuses on adaptive management of QoS-aware service composition in grid environments and proposes an adaptive algorithm for QoS-aware service composition (AQSC). In AQSC we model this problem as the Multi-Constrained Optimal Path selection problem (MCOP) and use heuristic approach for service selection, then backup services set is introduced as an adaptive mechanism so as to ensure the fulfillment of composite service when some candidate services fail or withdraw. Both theoretical analysis and simulation results indicate that AQSC has high composition success rate, finish rate and low cost.     

QoS-aware and multi-granularity service composition

Composition of Web services can be very complex, and usually involves multiple atomic services and varieties of message exchange patterns. Worst still, with the increasing amount of available services with varying granularity and quality, selecting the best combination of services becomes very challenging. This paper addresses the issues on multi-granularity service composition with awareness of the service quality. In particular, we consider how a new service composition plan is produced, while preserving its original observable behaviors of a service that are shown to the service user, by substituting the service with another service or a set of services of finer or coarser grain. The new plan aims to have services of better quality (if the corresponding underlying services are available). To achieve this, we firstly define a behavioral signature model to capture observable behaviors of services. We then present that two service composition plans are choreography equivalent if they comply with the same behavioral signature model. We then propose a behavioral extracting algorithm to obtain the behavioral signature model from a service composition plan. We also present a method to determine choreography equivalence. Finally we briefly describe our prototype implementation that captures all these proposed algorithms.     

QoS-aware service composition for distributed video surveillance

Quality of Service (QoS) is essential for the ubiquitous access of media services in real-time distributed video surveillance applications. To have ubiquitous access of desired media with emergency officials’ handheld devices, appropriate media transcoding services are required. Currently, it is challenging to select and compose these services for each of the devices to satisfy the desired QoS demand. To compose these media services so that video stream is available for target pervasive and smart devices, a composition algorithm is required. Thus, this paper presents a QoS-aware service composition algorithm to select the best composition for the target ubiquitous client so that it can optimally provide QoS to heterogeneous users. We have implemented a video surveillance prototype to demonstrate the performance of the proposed QoS-aware composition algorithm. Results from this prototype reveal that the approach is suitable for real-time video surveillance.     

Genetic programming for QoS-aware web service composition and selection

Web services, which can be described as functionality modules invoked over a network as part of a larger application are often used in software development. Instead of occasionally incorporating some of these services in an application, they can be thought of as fundamental building blocks that are combined in a process known as Web service composition. Manually creating compositions from a large number of candidate services is very time consuming, and developing techniques for achieving this objective in an automated manner becomes an active research field. One promising group of techniques encompasses evolutionary computing, which can effectively tackle the large search spaces characteristic of the composition problem. Therefore, this paper proposes the use of genetic programming for Web service composition, investigating three variations to ensure the creation of functionally correct solutions that are also optimised according to their quality of service. A variety of comparisons are carried out between these variations and two particle swarm optimisation approaches, with results showing that there is likely a trade-off between execution time and the quality of solutions when employing genetic programming and particle swarm optimisation. Even though genetic programming has a higher execution time for most datasets, the results indicate that it scales better than particle swarm optimisation.