Semantic Web service composition based on the interface matching of service processes

针对基于服务一级粗粒度的语义Web服务组合方法无法有效地发现满足用户需求的潜在的服务组合,导致组合成功率低的问题,提出了基于语义Web服务内部流程接口匹配的自动服务组合方法.该方法利用基于语义的Web服务描述语言(OWL-S)并在领域本体的支持下对Web服务语义进行描述,对服务与服务流程进行区分,将服务流程作为组合操作对象,通过计算不同的Web服务内部流程接口之间语义关联程度,遵从后继服务流程选择策略自动地生成能够满足用户需求的流程组合方案,并通过反向检索组合流程,消除组合方案中冗余的服务流程.通过一系列的仿真实验对该组合方法的成功率、效率等方面进行了验证,结果证明了该方法可以更加有效地根据用户请求自动生成服务组合方案.     

支持服务关联的全局最优服务选择算法

针对面向服务的军事综合电子信息系统候选服务的服务质量往往依赖于其他候选服务的问题,提出了一种支持服务关联的全局最优服务选择方法.该方法将基于QoS的军事信息服务选择问题建模为带QoS约束的多目标组合优化问题,采用基于独立成分分析(ICA)的多目标分布估计算法同时优化多个目标函数,最终产生一组满足约束条件的Pareto最优解服务组合集.对比实验结果表明,基于ICA的多目标分布估计算法通过概率图模型可以描述服务间的相互关系,可以更好地解决存在服务关联的组合服务选择问题,具有良好的有效性和可行性.     

基于QoS的Web服务发布和发现框架

传统Web服务发布与发现机制中,服务提供者和消费者仅以Web服务的功能性属性作为其发布和发现服务的依据,并没有考虑诸如价格、执行时间、可靠性等的非功能性属性,即QoS(Quality of Service)。提出一个基于QoS的Web服务发布和发现框架。该框架在传统Web服务三脚架构中引入QoS broker,以实现QoS信息的收集、测试、监控和管理;全面地从Web服务提供者、服务使用者和UDDI三个角度探讨支持QoS的服务发现和发布;实现QoS信息的动态收集、评估和管理,以确保Web服务发布和发现过程能够基于动态更新的QoS信息,使服务选择过程更加有效。     

基于QoS预测模型的Web服务局部选择方法

提出一种基于QoS预测模型的选择方法,该方法不仅能够通过基于信誉度或BP网络的QoS预测模型较准确地查找到真正满足用户QoS需求的服务,而且能够在没有完全满足用户QoS需求服务的情况下,推荐尽量相似的服务以供用户作进一步选择。     

MANET QoS介质访问控制策略MAQF/MAC及其仿真研究

要实现移动Ad-Hoc网络MANET民用化，必然解决其MAC层的QoS创立。MAQF／MAC是一种新的MANET自适应QoS介质访问控制策略，该策略可以实时动态地测试网络资源，及时调整业务流对资源的访问和占用。本文着重从仿真实现角度，对MAQF／MAC协议的原理，预留表结构，QoS路由，压缩算法等问题进行探讨。仿真结果表明，MAQF／MAC在资源利用率和分级服务的QoS保障能力等方面明显优于MACA／PR。     

一种QoS感知的语义Web服务组合群决策算法

针对QoS信息不确定和存在多个决策者的语义Web服务组合问题,基于多属性群决策理论给出了一个自治的语义Web服务组合群决策算法(AGSC).该算法能够对以实数型、区间型和语言型数据描述的复杂的QoS信息进行综合评估,从而为多客户提供正确、高效的决策支持,为其优选出最佳的组合服务执行计划.利用真实Web服务的质量对该算法进行了实验验证,结果表明该算法具有优秀的决策灵敏性和稳定性,并能有效地反映决策群中个体角色的变化.     

直觉模糊环境下考虑用户动态偏好的云服务选择方法研究

云制造平台上集中了大量云服务,如何为没有专业知识的用户选择合适的云服务是一个重要的挑战。云服务选择过程中,当用户处在产品生命周期的不同阶段时,用户偏好随时间动态发生变化,进而对云服务QoS属性的偏好程度发生变化,这将直接影响云服务选择结果。因此,本文提出一种考虑用户动态偏好的云服务选择方法。首先,云服务QoS属性存在多种数据格式,故使用直觉模糊数处理异构的QoS数据。其次,在直觉模糊环境下采用熵权法确定用户初始偏好,进而使用马尔可夫链确定用户的动态偏好。最后,在直觉模糊环境下采用VIKOR方法评估备选云服务。实验结果表明,本文方法可以有效地帮助处在产品不同生命周期的用户选择满意的服务,且在不同的场景下,本文提出的方法是可靠的。     

基于服务曲线的TDMA无线网络上行信道时隙调度算法

研究了采用TDMA接入方式的无线分组网络上行信道时隙分配策略,基于移动台的QoS要求,提出了一种自适应时隙分配算法。利用服务曲线对移动台的QoS要求进行了建模。算法的目标是在尽量满足所有移动台Qos要求的基础上提高系统的上行吞吐量。在无法满足所有移动台QoS要求的情况下,使得移动台实际发送的数据与满足服务曲线要求之间的偏差最小。仿真结果表明：该算法能够在系统吞吐量和系统偏差之间进行折衷,从而有效地平衡上述两个目标。     

组合服务选择中的服务QoS反演

针对组合服务选择中原子服务的服务质量(QoS)未知的情形,提出了服务QoS的反向计算问题,通过对问题的详细分析将其转化为计算原子服务归一化QoS的问题,进一步提出了根据组合服务的QoS反向计算各原子服务归一化的QoS的反演算法,使得在原子服务QoS未知的情况下基于QoS进行组合服务选择成为可能,并在反演算法的基础上提出...     

BDAaaS模式下面向近似查询的任务调度建模及优化算法

大数据分析即服务（big data analytics-as-a-service,BDAaaS）是基于云平台提供数据分析服务的新型业务模式,很多企业基于其业务特点愿意牺牲精确度换取更快且费用更低的查询结果。因此,BDAaaS可用数据分割等技术提供近似查询服务。用户对近似查询服务的响应速度要求高,导致服务水平协议（service level agreement,SLA）中对服务响应的约束也更严格,BDAaaS面临更大的违约赔付风险。首先,在服务响应速度要求高、计算资源有限、部分子任务具备顺序依赖关系的前提下,对于近似查询服务中的云任务调度问题,构建了面向收益优化的近似查询任务调度模型。其次,为解决模型中不同子任务的顺序依赖难题,提出了一种基于遗传算法与强化学习相融合的优化算法。该算法利用遗传算法求解带依赖关系的组合优化问题,强化学习在求解过程中动态调整遗传算法的关键参数,在SLA赔付风险下保证调度效率与质量。最后,以仿真实验模拟不同场景来验证所提算法的求解效果与性能。结果表明,该算法可在计算资源有限的条件下,获得SLA违约率更低且收益更高的任务调度方案,从而确保用户满意度和BDAaaS运...     

Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there is no such policy of cloud SLA monitoring to minimize SLA violations. In this work, we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts, for critical and non-critical parameters. The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation. This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach (RPAA) which will monitor SLA at runtime, analyze the SLA parameters and try to find the possibility of SLA violations. We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection. We have defined two main components of SLA-PRAA i.e., (a) Handler and (b) Accounting and Billing Manager. We have also described the function of both components through algorithms. The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.     

Non-proportional loading in sequentially linear analysis for 3D stress states

Sequentially linear analysis (SLA), a non-incremental-iterative approach towards finite element simulation of quasi-brittle materials, is based on sequentially identifying a critical integration point in the model, to reduce its strength and stiffness, and the associated critical load multiplier (λ_crit), to scale the linear analysis results. In this article, two novel methods are presented to enable SLA simulations for non-proportional loading situations in a three-dimensional fixed smeared crack framework. In the first approach, the cubic function in the load multiplier is analytically solved for real roots using trigonometric solutions or the Cardano method. In the second approach, the load multiplier is expressed as a function of the inclination of a potential damage plane and is deduced using a constrained optimization approach. The first method is preferred over the second for the validation studies due to computational efficiency and accuracy reasons. A three-point bending beam test, with and without prestress, and an RC slab tested in shear, with and without axial loads, are used as benchmarks. The proposed solution method shows good agreement with the experiments in terms of force-displacement curves and damage evolution.     

SLA表面PDA/CPP复合涂层的制备及其性能研究

在大颗粒喷砂酸蚀(SLA)工艺形成的三维多级嵌套孔洞结构纯钛表面, 利用聚多巴胺(PDA)的超强粘附性和二次修饰功能, 接枝生物大分子酪蛋白磷酸肽(CPP), 制备PDA/CPP复合涂层。采用FESEM、EDS、XPS、水接触角分析仪、体外仿生矿化测试以及人骨髓间充质干细胞(hBMSCs)实验对样品表面进行表征分析和性能测试。结果表明, 两步浸泡法成功在SLA表面制得PDA/CPP复合涂层。该涂层在保留SLA多孔形貌的同时, 使水接触角从47.7°减小到25.5°, 明显提高了SLA表面亲水性; 在模拟体液浸泡1 d后的修饰表面即沉积了致密羟基磷灰石(HA), 说明PDA/CPP显著加快了SLA表面生物矿化的速度。同时, SLA/PDA/CPP表面细胞粘附、增殖以及矿化活性都得到了明显改善。     

OFDMA系统中支持混合业务的跨层调度算法

针对正交频分多址接入(OFDMA)系统下行链路的混合业务调度问题,提出了一种基于队列等待时间的跨层调度算法.该算法联合利用了MAC层的队列等待时间与物理层的信道状态信息作为调度参数,通过队列等待时间反映用户的服务质量要求,并利用多用户分集增益提高系统性能;针对实时和非实时用户的不同服务质量要求,在队列等待时间的计算上采取了不同的策略;在子载波的分配过程中根据分配状态及时更新队列等待时间,使资源的利用更为有效.仿真结果表明,提出的算法可显著降低实时用户的平均时延和最大延时违反概率,同时保证了非实时用户的吞吐量需求,能够有效地支持下一代网络中混合业务的多种服务质量要求.     

Prediction of Web Services Reliability Based on Decision Tree Classification Method

With the development of the service-oriented computing (SOC), web service has an important and popular solution for the design of the application system to various enterprises. Nowadays, the numerous web services are provided by the service providers on the network, it becomes difficult for users to select the best reliable one from a large number of services with the same function. So it is necessary to design feasible selection strategies to provide users with the reliable services. Most existing methods attempt to select services according to accurate predictions for the quality of service (QoS) values. However, because the network and user needs are dynamic, it is almost impossible to accurately predict the QoS values. Furthermore, accurate prediction is generally timeconsuming. This paper proposes a service decision tree based post-pruning prediction approach. This paper first defines the five reliability levels for measuring the reliability of services. By analyzing the quality data of service from the network, the proposed method can generate the training set and convert them into the service decision tree model. Using the generated model and the given predicted services, the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service. Moreover, this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting. Experimental results show that the proposed method is effective in predicting the service reliability.     

Combining social network and collaborative filtering for personalised manufacturing service recommendation

Owing to the rapid proliferation of Web service technologies in cross-enterprise manufacturing collaborations, information overload is becoming a major barrier that hinders the effective discovery of the shared manufacturing services provided by collaborative partners for supply chain deployment. Thus, we aimed to identify a different approach for discovering manufacturing services by making personalised service recommendations that are suited to the specific needs of active service users based on usage data from previous retrievals made by past service users. The proposed approach combines social network and collaborative filtering techniques in a unified framework to predict the missing Quality of Service (QoS) values of manufacturing services for an active service user, thereby improving the effectiveness of personalised QoS-aware service recommendations. The social network explores the usage of preference and tagging relationships among service users and manufacturing services in making personalised recommendation, which alleviates the data sparsity and the cold start problems that hinder the traditional collaborative filtering techniques. A case study and experimental evaluation demonstrate that the proposed approach can achieve the practicality and accuracy to personalised manufacturing service recommendations in a real application.     

Delivery of pre‐stored videos for mobile clients with insufficient playback buffer and bounded delay guarantee

Abstract

A deterministic service model assisted by a sufficiently large playback buffer space can provide bounded delay guarantees for video packets and simplify network resource management. However, many popular mobile terminals do not have sufficient memory capacity for deterministic video services since the embedded memory is limited and needs to be shared by numerous software programs and masses of personal data. This paper improves the traditional deterministic modeling approach for delivering pre‐stored videos to mobile clients with QoS guarantees. The limitation of playback buffer space, the network delay jitter, the processing load of resource management, and the QoS guarantee are considered in the proposed mechanism. Some traffic smoothing operations are integrated into the proposed mechanism for reducing the playback buffer demand and data rate variation. This paper further proposes a smart video frame skip algorithm, originating at the sender for preventing possible overflow problems due to insufficient playback buffer space. The algorithm can determine the most suitable temporal range for skipping frames and prevent arbitrary discarding from inappropriate video frames such as I‐frames on the client side. Simulation results reveal that the proposed mechanism can effectively remedy situations of insufficient playback buffer space while still maintaining the advantages of deterministic services.     

基于P2P的空间信息服务组合执行引擎

提出了一种基于P2P的空间信息服务组合执行引擎设计.引擎使用空间信息服务团体模型和服务质量参数,在P2P网络中动态地选择最佳的执行引擎,通过P2P的消息和通信机制进行协作,完成复杂的空间信息服务组合执行任务.P2P的执行机制避免了集中式执行引擎带来的网络拥塞和单点失效问题,提高了空间信息服务组合的可靠性和可用性.     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.     

Cooperative wireless networks for better green services

During the last two decades, mobile communication systems (such as GSM, GPRS and 3G networks), wireless broadcasting networks, wireless local area networks (WLAN or WiFi), and wireless sensor networks have been successfully developed and widely deployed through different technological routes for providing a variety of communication services in different application scenarios. While making tremendous contributions to social progress and economic growth, these heterogeneous wireless networks consume a lot of energy in achieving overlapped service coverage, and at the same time, generate strong electromagnetic interference (EMI) and radiation pollution, especially in big cities with high building density and user population. In order to guarantee the overall return on investment (ROI), improve user experience and quality of service (QoS), save energy, reduce EMI and radiation pollution, and enable the sustainable deployment of new profitable applications and services, this paper proposes a cross-network cooperation mechanism to effectively share network resources and infrastructures, and then adaptively control and match multi-network energy distribution characteristics according to actual user/service requirements in different geographic areas. Some idle or lightly-loaded Base Stations (BS or BSs) will be temporally turned off for saving energy and reducing EMI. Initial simulation results show the proposed approach can significantly improve the overall energy efficiency and QoS performance across multiple cooperative wireless networks.