基于服务簇和QoS的Web服务组合方法

针对海量的语义Web 服务组合中如何提高搜索速度和获得最优组合的问题,提出了一种基于服务簇和服务质量(QoS)的快速组合方法。利用预先建立的服务簇进行服务搜索,可以有效缩小搜索空间,降低语义比较的复杂度,快速得到候选服务集合。组合过程中基于服务的最优组合QoS值动态确定阈值进行服务过滤,可以获得多个最优组合。同时采用高效的冗余处理方法保证组合中冗余服务最少,并采用服务簇内部过滤的方法限制候选服务数量,解决了因组合过多造成的运行超时问题。基于海量服务库进行实验,结果表明,服务搜索效率比普通搜索方法可提高几十倍,服务过滤和冗余处理效果明显,可快速获得多个QoS最优的无冗余组合,并且在百万级服务库进行大层次深度的组合时仍可保证良好的运行性能。     

基于服务关系统计的多粒度服务组合方法

传统工作流方法进行服务组合存在两个问题,服务组合无法自动生成和复杂服务无法重用,基于服务关系统计的多粒度服务组合方法（MSWC）有效解决了这两个问题。MSWC包括服务关系统计学习、服务粗分和服务细分三个部分, 分别用于进行服务关系统计计算、服务分组和服务映射。通过服务关系统计学习计算了有逻辑和无逻辑的服务关联度;服务粗分将服务需求分解为无逻辑关系的组;而服务细分通过逻辑概率将服务分组映射成系统中已有的复杂服务,将这些复杂服务串接起来,即完成服务组合。因为三个步骤均是自动执行,因此MSWC是自动组合方法,而服务分组最终映射为系统已有的复杂服务,因此是一个多粒度服务组合方法。最后通过实验结果分析表明,该算法能够很好地适应网络上Web Service快速增长的情况,并且具备较好的服务组合性能。     

Indeterminacy-aware service selection for reliable service composition

With the development of Internet and Web service technology, Web service composition has been an effective way to construct software applications; service selection is the crucial element in the composition process. However, the existing selection methods mostly generate static plans since they neglect the inherent stochastic and dynamic nature of Web services. As a result, Web service composition often inevitably terminates with failure. An indeterminacy-aware service selection algorithmbased on an improved Markov decision process (IMDP) has been designed for reliable service composition, but it suffers from higher computation complexity. Therefore, an efficient method is proposed, which can reduce the computation cost by converting the service selection problem based on IMDP into solving a nonhomogeneous linear equation set. Experimental results demonstrate the success rate of service composition has been improved greatly, whilst also reducing computation cost.     

A fast and scalable approach for IoT service selection based on a physical service model

Information Systems (ISs) have become one of the crucial tools for various organizations in managing and coordinating business processes. Now we are entering the era of the Internet of Things (IoT). IoT is a paradigm in which real-world physical things can be connected to the Internet and provide services through the computing devices attached. The IoT infrastructure is starting to be integrated with ISs thereby diminishing the boundaries between the physical world and the business IT systems. With the development of IoT technologies, the number of connected things and their available physical services are increasing rapidly. Thus, selecting an appropriate service that satisfies a user’s requirements from such services becomes a time-consuming challenge. To address this issue, we propose a Physical Service Model (PSM) as a common conceptual model to describe heterogeneous IoT physical services. PSM contains three core concepts (device, resource, and service) and specifies their relationships. Based on the proposed PSM, we define three types of Quality of Service (QoS) attributes and rate candidate services according to user requirements. To dynamically rate QoS values and select an appropriate physical service, we propose a Physical Service Selection (PSS) method that takes a user preference and an absolute dominance relationship among physical services into account. Finally, experiments are conducted to evaluate the performance of the proposed method.     

基于QoS模糊支配的并发服务组合方法

针对当前服务组合研究主要考虑单个用户请求而忽视并发请求的问题,提出一种基于QoS模糊支配的并发服务组合方法。根据QoS模糊支配关系,提出模糊支配得分的概念,以衡量服务的整体质量。在服务组合过程中,从每个组件中选出模糊支配得分最高的k个服务,去匹配批量的用户请求。对于每个组合请求,从每个相关组件的top-k服务中随机选择一个服务参与服务组合。实验结果表明,该方法可为用户提供较高质量的组合服务,具有较好的实时性。     

基于接口自动机的混合Web服务组合方法

在Web服务描述语言(WSDL)与网络服务的本体语言(OWL-S)共存的环境中难以实现Web服务混合组合,为此提出一种以接口自动机为基础的混合Web服务组合方法。分析和描述WSDL与OWL-S间的关系后,在服务组合过程中用接口自动机实现WSDL与OWL-S自动识别和组合;并将服务组合结果与预先设定好的服务质量进行优化对比,选择服务质量最优的来实现不同的业务逻辑。最后通过一组旅游消费服务实验分析验证表明,该方法可行、有效,服务组合效率提高了5%~10%。     

一种模型驱动的WS-CDL服务组合可靠性预测方法

WS-CDL是一种主流的Web服务组合标准,描述了基于编排机制的端对端服务组件的组合方式。对基于WS-CDL的服务组合进行可靠性预测,能帮助Web服务的使用和管理者定量考察服务的可信性,并帮助他们从功能相同的服务组件中择优选取以优化整体服务流程的可靠性。然而,学术界对WS-CDL可靠性研究非常匮乏。提出了一种模型驱动的方法来预测基于WS-CDL的组合服务的可靠性。该方法首先引入一系列的转换规则,将WS-CDL中的各种活动和结构转换为等效的NMSPN网描述。在生成的NMSPN网基础上,引入了一个预测算法以分析组合服务的“流程正常结束概率”。为了对理论模型和结果进行检验,还通过WS-CDL+执行引擎对服务组合的实例进行执行并获得了流程正常结束率的实验数据。比较发现,由实验数据导出的95%置信区间完好地覆盖了理论分析结果,表明了该方法的正确性和精确性。     

基于成本效益系数的Web服务组合可靠性优化方法

为了解决Web服务组合优化过程计算量大、非线性等问题,提出在给定成本约束下利用成本效益系数(CBC)对Web服务组合可靠性进行优化的方法。首先分析了Web服务组合结构模式及其对应的可靠性函数,进一步给出服务组合的可靠性计算方法,并建立非线性可靠性优化模型;然后,通过组件服务的成本及其可靠性关系计算成本效益系数,进一步确定Web服务组合的优化方案,再根据优化模型计算得到优化结果;最后,以组件服务可靠性数据为基础,通过与传统的优化方法比较,验证了基于成本效益系数的方法可以在给定成本约束时获得更高的可靠性。实验结果表明,所提方法对Web服务组合进行可靠性优化是有效、合理的。     

一种快速易用的基于行为描述的服务组合方法*

为了提高基于行为描述的服务组合方法的易用性和效率,首先对服务和服务请求进行建模;然后使用一个三层图模型组织服务之间的关系,并基于这个三层图模型提出一种快速易用的服务组合方法;最后通过实例说明了该方法的组合过程,并从理论上分析了该方法的时间复杂度。理论分析显示该方法具有较好的效率。     

基于轮廓线度量的形态学骨架剪枝方法

提出了一种基于轮廓线度量的骨架剪枝方法,该方法使用距离骨架点最近的轮廓点在轮廓线上的最小距离作为骨架点显著性的度量,该度量具有较好的显著性表现能力、多余的毛刺状分枝区分能力和公平性.在算法中将轮廓线上的所有点建立为kd-树,通过kd-树搜索距离骨架点最近的轮廓点.将此方法运用到形态学细化产生的骨架上,通过在最近骨架点搜索和距离计算上引入一定程度的平滑,得到效果良好的图像骨架.实验结果表明该方法有较强的稳定性和抗噪能力.     

基于服务模式的快速服务组合方法研究

多数传统服务组合方法忽视了领域服务的特性,针对特定的领域服务上下文环境时无法进一步提高优化性能,对此提出一种基于模式的快速服务组合方法。分析了领域服务的特征和规律,提出了服务模式的概念。进而给出了一种基于模式的两阶段快速服务组合方法,该方法在第一阶段利用已有的服务模式,采用贪心覆盖策略对用户需求进行快速覆盖,第二阶段则采用原子服务满足第一阶段无法满足的需求。实验表明利用该方法能获得良好的效果和性能。     

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.     

动态Web服务合成中的服务选择算法研究

为了提高服务合成效率并更好的适应动态变化的应用环境,提出了基于流程修改的服务合成方案,使得复合服务的执行与服务发现过程并行化。提出了基于QoS属性的服务选择算法,该算法能够实现服务执行时选择服务,在考虑综合QoS信息及时间因素的基础上,通过不断更新用户需求来进行服务选择,提高了服务合成效率。实验结果表明,该选择算法可以获得满足用户需求且最优的服务,很好的保证了服务的可靠执行。     

A service composition approach based on sequence mining for migrating e-learning legacy system to SOA

With the fast development of business logic and information technology, today's best solutions are tomorrow's legacy systems. In China, the situation in the education domain follows the same path. Currently, there exists a number of e-learning legacy assets with accumulated practical business experience, such as program resource, usage behaviour data resource, and so on. In order to use these legacy assets adequately and efficiently, we should not only utilize the explicit assets but also discover the hidden assets. The usage behaviour data resource is the set of practical operation sequences requested by all users. The hidden patterns in this data resource will provide users' practical experiences, which can benefit the service composition in service-oriented architecture (SOA) migration. Namely, these discovered patterns will be the candidate composite services (coarse-grained) in SOA systems. Although data mining techniques have been used for software engineering tasks, little is known about how they can be used for service composition of migrating an e-learning legacy system (MELS) to SOA. In this paper, we propose a service composition approach based on sequence mining techniques for MELS. Composite services found by this approach will be the complementation of business logic analysis results of MELS. The core of this approach is to develop an appropriate sequence mining algorithm for mining related data collected from an e-learning legacy system. According to the features of execution trace data on usage behaviour from this e-learning legacy system and needs of further pattern analysis, we propose a sequential mining algorithm to mine this kind of data of the legacy system. For validation, this approach has been applied to the corresponding real data, which was collected from the e-learning legacy system; meanwhile, some investigation questionnaires were set up to collect satisfaction data. The investigation result is 90% the same with the result obtained through our approach.     

Iterative selection algorithm for service composition in distributed environments

In service oriented architecture （SOA）, service composition is a promising way to create new services. However, some technical challenges are hindering the application of service composition. One of the greatest challenges for composite service provider is to select a set of services to instantiate composite service with end- to-end quality of service （QoS） assurance across different autonomous networks and business regions. This paper presents an iterative service selection algorithm for quality driven service composition. The algorithm runs on a peer-to-peer （P2P） service execution environment--distributed intelligent service execution （DISE）, which provides scalable QoS registry, dynamic service selection and service execution services. The most significant feature of our iterative service selection algorithm is that it can work on a centralized QoS registry as well as cross decentralized ones. Network status is an optional factor in our QoS model and selection algorithm. The algorithm iteratively selects services following service execution order, so it can be applied either before service execution or at service run-time without any modification. We test our algorithm with a series of experiments on DISE. Experimental results illustrated its excellent selection and outstanding performance.     

Cloud computing system risk estimation and service selection approach based on cloud focus theory

The main cloud computing service providers usually provide cross-regional and services of Crossing Multi-Internet Data Centers that supported with selection strategy of service level agreement risk constraint. But the traditional quality of service (QoS)-aware Web service selection approach cannot ensure the real-time and the reliability of services selection. We proposed a cloud computing system risk assessment method based on cloud theory, and generated the five property clouds by collecting the risk value and four risk indicators from each virtual machine. The cloud backward generator integrated these five clouds into one cloud, according to the weight matrix. So the risk prediction value is transferred to the risk level quantification. Then we tested the Web service selection experiments by using risk assessment level as QoS mainly constraint and comparing with LRU and MAIS methods. The result showed that the success rate and efficiency of risk assessment with cloud focus theory Web services selection approaches are more quickly and efficient.

     

Time-aware prefetching for on-demand video services in a residential service gateway

With the recent advances in network technology, the number of high-speed networked homes increases rapidly and the enhanced services such as on-demand video services become feasible in terms of market maturity. Another trend is that storage systems become network-accessible. One of the leading network-attached storage systems is the Fiber Channel Arbitrated Loop (FC-AL). As a residential service gateway, the FC-AL-based servers can stably provide high quality video (e.g., DVD quality MPEG-2 stream) with thousands of clients between external service providers and local clients. In addition, in densely populated areas such as New York City, they can be much more cost efficient. Using our end-to-end simulation experiments to combine all the components, we have observed that FC-AL-based streaming servers perform better than SCSI-based systems, but there is still room for performance improvement. We are motivated by the fact that, unlike in SCSI-based systems, all the disks in FC-AL-based severs utilize only a small portion of their caches to a similar degree due to FC-AL fairness arbitration algorithm. Thus, we propose an effective prefetching scheme to improve the performance by further utilizing the disk cache. We show how the proposed scheme can determine the maximum number of prefetched blocks depending on the disk block and cache size. It is also shown how to find the optimal number of blocks transmitted to the FC-AL from the disk cache per FC-AL arbitration. In addition, we describe the cache replacement policy to take full advantage of the sequential access pattern of video files, and explain how to support multiple loops. By analysis and simulation experiments, we show that our prefetching scheme is not able to only increase the total number of concurrent streams significantly by reducing the disk seek time, but it can also further utilize the FC-AL by reducing the overhead of arbitration.