A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.     

Towards a trust evaluation middleware for cloud service selection

With the advent of cloud computing, employing various cloud services to build highly reliable cloud applications has become increasingly popular. The trustworthiness of cloud services is a critical issue that hinders the development of cloud applications, and thus is an urgently-required research problem. Previous studies evaluate trustworthiness of services via either QoS monitoring mechanisms or user feedback ratings, while seldom they combine both of them for enhancing service trust evaluation. This paper proposes a trustworthy selection framework for cloud service selection, named TRUSS. Aiming at developing an effective trust evaluation middleware for TRUSS, we propose an integrated trust evaluation method via combining objective trust assessment and subjective trust assessment. The objective trust assessment is based on QoS monitoring, while the subjective trust assessment is based on user feedback ratings. Experiments conducted using a synthesized dataset show that our proposed method significantly outperforms the other trust and reputation methods.     

基于QoS的可信Web服务组合研究

如何合理、高效地选择和组合众多功能相似的服务,为用户提供更优质的服务,是当前SOA领域研究的重点和难点。服务质量QoS(Quafity of Service)的引入,在一定程度上保证了服务组合的合理性,但对于如何确保服务组合的可信,目前还没有一个比较完善和可行的方案。因此,提出了可信Web服务组合模型(Trust Web Service Composition Modcl,TWSCM),并通过在原有Wcb服务体系架构上增加QoS代理和可信模块来确保服务组合的质量和可信。最后,在语言网格上进行了相关实验,结果表明,该方法能为用户提供高质量、可信的组合服务,从而验证了该方法的合理性和有效性。     

属性群签名的可信服务选择方案

面向服务计算(service-oriented computing,SOC)的一个基本内容就是选择和组合服务以直接支持指定用户的需求或提供附加服务,这些需求往往是细粒度的.现有的服务选择方法要么没有考虑要么是从粗粒度上考虑服务间的信任关系.我们提出一个新的可信服务选择方法:基于属性群签名的可信服务选择方案(TSS-ABGS).该方案将功能属性相同的可选服务组成一个属性群,当请求者向该群发出包含属性集的选择请求时,满足属性集要求的群成员返回一个签名,请求者通过群管理员开放签名者并与之建立可信关系而达到可信服务选择的目的.该方法具有可跟踪性和防共谋.     

基于云模型的不确定性QoS 感知的Skyline 服务选择

由于传统QoS感知的Web服务选择方法无法保证服务选择的可靠性和实时性,提出了一种基于云模型的不确定性QoS感知的Skyline服务选择方法.该方法首先通过云模型计算QoS的不确定性,然后采用Skyline计算提取Web服务中的Skyline服务,剔除冗余服务,最后采用混合整数规划在Skyline服务中进行服务选择.在公共有效数据集和合成数据集上的实验结果表明,所提出的方法能够为用户提供可靠、快速的服务选择.     

基于领域本体的可信服务组合

针对现有Web服务组合方法缺乏对服务自身属性信任问题的深入考虑,服务组合过程可信需求无法得到保障,提出了一种基于领域本体的可信Web服务组合方法。将服务的非功能属性分为服务质量、服务信任和服务上下文三个主要方面,并采用本体构建工具Protégé构建原子服务本体,利用推理机FaCT++对原始服务集进行本体推理过滤;引入了融合本体推理过滤的Web服务架构;提出了基于层次任务网络(hierarchical tasknetwork,HTN)规划法的可信服务组合算法。仿真实验表明:与传统的组合方法相比,该方法可有效提高服务组合的效率和成功率。     

Probabilistic approach for QoS-aware recommender system for trustworthy web service selection

We present a QoS-aware recommender approach based on probabilistic models to assist the selection of web services in open, distributed, and service-oriented environments. This approach allows consumers to maintain a trust model for each service provider they interact with, leading to the prediction of the most trustworthy service a consumer can interact with among a plethora of similar services. In this paper, we associate the trust in a service to its performance denoted by QoS ratings instigated by the amalgamation of various QoS metrics. Since the quality of a service is contingent, which renders its trustworthiness uncertain, we adopt a probabilistic approach for the prediction of the quality of a service based on the evaluation of past experiences (ratings) of each of its consumers. We represent the QoS ratings of services using different statistical distributions, namely multinomial Dirichlet, multinomial generalized Dirichlet, and multinomial Beta-Liouville. We leverage various machine learning techniques to compute the probabilities of each web service to belong to different quality classes. For instance, we use the Bayesian inference method to estimate the parameters of the aforementioned distributions, which presents a multidimensional probabilistic embodiment of the quality of the corresponding web services. We also employ a Bayesian network classifier with a Beta-Liouville prior to enable the classification of the QoS of composite services given the QoS of its constituents. We extend our approach to function in an online setting using the Voting EM algorithm that enables the estimation of the probabilities of the QoS after each interaction with a web service. Our experimental results demonstrate the effectiveness of the proposed approaches in modeling, classifying and incrementally learning the QoS ratings.     

Recommend trustworthy services using interval numbers of four parameters via cloud model for potential users

How to discover the trustworthy services is a challenge for potential users because of the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. Aiming to the limitations of traditional interval numbers in measuring the trustworthiness of service, this paper proposed a novel service recommendation approach using the interval numbers of four parameters (INF) for potential users. In this approach, a trustworthiness cloud model was established to identify the eigenvalue of INF via backward cloud generator, and a new formula of INF possibility degree based on geometrical analysis was presented to ensure the high calculation precision. In order to select the highly valuable QoE evaluations, the similarity of client-side feature between potential user and consumers was calculated, and the multi-attributes trustworthiness values were aggregated into INF by the fuzzy analytic hierarchy process method. On the basis of ranking INF, the sort values of trustworthiness of candidate services were obtained, and the trustworthy services were chosen to recommend to potential user. The experiments based on a realworld dataset showed that it can improve the recommendation accuracy of trustworthy services compared to other approaches, which contributes to solving cold start and information overload problem in service recommendation.     

一种考虑QoS数据可信性的服务选择方法

随着Internet上功能相似的Web服务的逐渐增多,在运行时刻基于服务质量(QoS)对Web服务进行查找和选择已成为研究热点.现有的基于QoS的服务选择方法通常假定服务提供者和使用者给出的QoS数据都是真实可信的,然而这一假设在实际中往往很难保证.为此,提出了一种考虑QoS数据可信性的服务选择方法.方法从QoS数据来源的角度对质量属性进行分类和计算:对于数据来自服务提供者的质量属性,使用以往运行数据统计,对提供者的QoS数据进行修正;对于数据来自服务使用者的质量属性,通过计算用户间以往反馈的相似程度权衡不同QoS反馈数据的可信程度.对此给出了实现框架,并通过一组模拟实验说明该方法能够有效地削弱不可信的QoS数据对服务选择的影响,增强了Web服务选择结果的准确性.     

一种QoS感知的Web服务群体评价方法

传统QoS感知的Web服务选择方法需要用户给出精确权重以表达其对QoS属性的偏好。为解决用户无明确偏好情况下的服务选择问题,提出了一种QoS感知的Web服务群体评价方法。对Web服务群体评价的需求进行了详细的分析,以有明确QoS偏好的用户对服务的历史选择结果为基础,利用群决策的Condorcet和Borda函数集结这些结果得到服务的群体评价,从而使得无明确QoS偏好用户可以参考该评价结果进行服务选择。理论分析和仿真实验验证了该服务群体评价方法的合理性和有效性。     

COCOA: COnversation-based service COmposition in pervAsive computing environments with QoS support

Pervasive computing environments are populated with networked services, i.e., autonomous software entities, providing a number of functionalities. One of the most challenging objectives to be achieved within these environments is to assist users in realizing tasks that integrate on the fly functionalities of the networked services opportunely according to the current pervasive environment. Towards this purpose, we present COCOA, a solution for COnversation-based service COmposition in pervAsive computing environments with QoS support. COCOA provides COCOA-L, an OWL-S based language for the semantic, QoS-aware specification of services and tasks, which further allows the specification of services and tasks conversations. Moreover, COCOA provides two mechanisms: COCOA-SD for the QoS-aware semantic service discovery and COCOA-CI for the QoS-aware integration of service conversations towards the realization of the user task’s conversation. The distinctive feature of COCOA is the ability of integrating on the fly the conversations of networked services to realize the conversation of the user task, by further meeting the QoS requirements of user tasks. Thereby, COCOA allows the dynamic realization of user tasks according to the specifics of the pervasive computing environment in terms of available services and by enforcing valid service consumption.     

A rough set-based hypergraph trust measure parameter selection technique for cloud service selection

Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking.     

基于可信QoS的路径查找及负载均衡策略研究

Web服务是云计算中资源调用的有效方式。单一Web服务功能往往有限,只能完成特定任务。服务组合则可以将多种Web服务形成有效的调用序列,实现更为强大的功能。服务发布量以及服务请求量的迅速激增带来了新的安全问题。首先,现有的服务组合方案均以服务质量(QoS)为依据进行Web服务选择,但服务质量通常由服务发布者提供,存在服务发布者发布虚假QoS值诱骗用户的欺诈现象;其次,传统的服务组合方案只生成一条最优路径,当恶意请求持续访问时,会造成某服务节点瘫痪,甚至整个服务组合系统失效。因此,针对服务质量恶意欺诈的问题,文章提出一种可信的QoS计算模型,根据Web服务发布者的信用综合评估服务质量;针对单一最优路径无法满足大量请求的问题,文章提出一种路径发现和负载均衡的多路径方法。仿真结果表明,文章提出的方法不仅能提高服务组合的成功率,满足用户的需求,而且能找到更多的服务组合方案执行。     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.     

QoS可信的服务组合技术研究*

现有基于QoS的服务组合方法通常假定QoS数据可信,然而这种假设很难保证,虚假的服务对于服务组合实现的影响常常是致命的。针对该问题,提出一种考虑QoS可信的服务组合方法。分析了QoS来源,建立了相应的系统结构,划分了信任级别,根据社会交往方式量化了直接经验和间接经验在可信判断中的作用,并以此构建QoS的计算模型,设计了相应的算法。最后,通过仿真实验,将考虑可信的服务组合方法与未考虑可信的传统组合方法进行了比较,前者体现出明显的优越性,从而表明提出的方法在性能和执行效率上是可行的。     

支持QoS属性的Web服务PFS模型研究

目前的Web服务只有基于功能性描述的服务注册与发现接口，不能实现具有QoS属性的Web服务注册与发现，为此提出了支持QoS属性的Web服务PFS模型，引入QoS Broker模块实现具有QoS属性的Web服务注册、查找、选择和QoS信息的管理功能，实现了Web服务使用过程中的QoS监视以及QoS评价，并将最新的评价结果用于服务QoS信息的更新，从而保证了QoS信息的公平性、可信性和实时性。     

User feature-aware trustworthiness measurement of cloud services via evidence synthesis for potential users

Cloud computing can provide elastic and dynamic resources on demand, which facilitates service providers to make profits resulting from the long tail effect. It becomes vitally important to ensure that cloud services can be acceptable to more potential users. However, it is challenging for potential users to discover the trustworthy cloud services due to the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. This paper presents a user feature-aware trustworthiness measurement approach for potential users. In this approach, the influence factors of QoE are systematically analyzed based on the user feature model and the quantitative computation methods are designed to measure the user feature similarity. In addition, employing FAHP (fuzzy analytic hierarchy process) method identifies the user feature community. To enhance the accuracy of trustworthiness measurement, the false evidences in QoE evaluations are iteratively filtered out with dynamic mean distance threshold. Finally, the trustworthiness of service is measured via evidence synthesis combining user feature similarity. The experiments show that this approach is effective to improve the quality of trustworthiness measurement, which is helpful to solve information overload problem and cold start problem of trusted service recommendation for potential users.     

基于滑动窗口的云服务可信评估模型优化

随着云服务的需求日益增强,云服务市场得到了快速的发展,但也产生了云服务的可信问题.国内外针对这个问题提出了各类的云服务可信评估模型,使用直接间接可信,用户评价、偏好相似度等因素参与云服务可信评估的流程中,然而现有研究中的云服务可信评估模型在流程中针对信任评估中并不能满足像是缓慢增加和快速下降等基本性质.本文引入云服务可信滑动窗口的方式加强整个云服务可信评估的流程,通过滑动窗口满足了缓慢增长等性质解决了问题,而后通过模拟实验验证了确实能够有效解决该问题.     

基于信誉的语义Web服务发现

服务发现在语义Web领域是一个挑战性问题.一个关键问题是发现满足用户功能需求的最佳服务.同样重要的是,电子商务应用还要求发现满足QoS需求的最佳服务.由此提出一个基于信誉管理的分布式服务选择框架.它主要有以下特点：用一个结构化的覆盖网络作为分布式服务存储系统来提高系统的有效性和可扩展性; 提出一个QoS感知的语义Web服务发现方法,并给出基于用户反馈的分布式信誉度评估算法.最后用一个例子示范算法如何解决实际问题.     

基于不确定服务质量感知的云服务组合方法

针对不确定服务质量（QoS）感知的云服务组合优化问题的求解,提出一种不定长时间序列（ULST）模型和锦标赛策略的改进遗传算法（T-GA）。首先,基于用户对服务不同时间段的访问规律,将服务质量的长期变化构建为不定长时间序列模型,该模型能够准确地描述一段时间内用户对服务的真实QoS访问记录。其次,提出一种基于不确定QoS模型的改进遗传算法,该算法采用锦标赛选择策略代替基本遗传算法中的轮盘赌选择策略。最后,在真实数据上进行了大量实验,所提的不定长时间序列模型能够有效地解决不确定QoS感知云服务组合问题,而锦标赛策略的改进遗传算法在寻优结果和稳定性方面均优于基于精英选择策略的遗传算法（E-GA）算法,且运行速度提高近1倍,是可行、高效且稳定的算法。