基于时间感知排序的云服务QoS预测方法研究

随着云计算理论和技术的成熟,越来越多的云服务得到了蓬勃发展,如何建立高质量的云服务成为了云计算研究领域的一个关键难题。服务质量QoS排序为用户从一系列功能相似的云服务候选者中挑选最优云服务提供了非常有价值的信息。为了获得云服务的QoS值,就需要调用真实的候选云服务。为了避免时间消耗和昂贵的资源浪费,提出了一种基于时间感知排序的云服务QoS预测方法。不同于传统的QoS值预测,基于QoS排序相似度的预测考虑为特定用户检测服务的排序。分时段按权计算出排序相似度,结合时间偏好合成相似度的前k位用户,用来提供信息支持QoS的缺失预测。在WS Dream真实数据集进行的实验研究表明,基于时间感知排序的云服务QoS预测方法有更好的预测精度。     

From similarity perspective: a robust collaborative filtering approach for service recommendations

Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions.We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.     

基于贝叶斯张量分解的改进QoS预测

随着Web服务相关标准和技术的日趋成熟,基于服务质量（QoS）的Web服务推荐对用户体验起着决定性作用。如何准确预测Qos值是当今的研究热点。以往基于近邻或模型的协同过滤算法,采用的是“用户-服务”二维信息,预测的QoS值是静态的且精准性不高。将时间信息维度引入张量模型,建立“用户-服务-时间”的三维张量可使QoS预测值更加符合用户需求特点,用贝叶斯方法求解张量分解,引入概率意义下对于系统的解释和分析,提供一套先验概率引入先验知识的贝叶斯推断框架,提高了QoS预测的精确度。实验表明,使用该算法的预测结果较其他算法相比较有更小的平均绝对误差,很好地解决了数据稀疏度问题。     

基于关联规则挖掘与用户情景感知的Web服务QoS预测

作为Web服务的非功能性属性,Qo S在服务选择与服务组合中扮演着重要角色。由于一些Qo S属性值会随着用户情景属性的变化而动态变化,因此在做服务选择或服务组合之前先对Web服务的Qo S做预测是非常必要的。该文提出运用数据挖掘技术挖掘服务组合执行日志,研究服务Qo S和用户情景间的关联关系,根据得到的关联规则对不同用户做个性化服务预测。实验结果表明我们的方法非常有研究价值。     

一种基于用户情境的QoS预测方法

完整的QoS信息有利于更准确的服务推荐,但是现实中往往很难得到。文章提出了一种基于用户情境的QoS预测方法,对于老用户,根据他们原来的QoS选择,考虑QoS类型区别和时间衰减情况,预测新的QoS取值;对于新用户,按照用户分类信息,根据同类用户的服务选择情况,预测他们的QoS取值。实验证明,该方法有助于提高服务推荐的性能。     

Graph kernel based link prediction for signed social networks

By revealing potential relationships between users, link prediction has long been considered as a fundamental research issue in singed social networks. The key of link prediction is to measure the similarity between users. Existing works use connections between target users or their common neighbors to measure user similarity. Rich information available for link prediction is missing since use similarity is widely influenced by many users via social connections. We therefore propose a novel graph kernel based link prediction method, which predicts links by comparing user similarity via signed social network’s structural information: we first generate a set of subgraphs with different strength of social relations for each user, then calculate the graph kernel similarities between subgraphs, in which Bhattacharyya kernel is used to measure the similarity of the k-dimensional Gaussian distributions related to each k-order Krylov subspace generated for each subgraph, and finally train SVM classifier with user similarity information to predict links. Experiments held on real application datasets show that our proposed method has good link prediction performances on both positive and negative link prediction. Our method has significantly higher link prediction accuracy and F1-score than existing works.     

CluCF: a clustering CF algorithm to address data sparsity problem

In QoS-based Web service recommendation, predicting Quality of Service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. Data sparsity is an important challenges for CF algorithms. Although model-based algorithms can address the data sparsity problem, those models are often time-consuming to build and update. Thus, these CF algorithms aren’t fit for highly dynamic and large-scale environments, such as Web service recommendation systems. In order to overcome this drawback, this paper proposes a novel approach CluCF, which employs user clusters and service clusters to address the data sparsity problem and classifies the new user (the new service) by location factor to lower the time complexity of updating clusters. Additionally, in order to improve the prediction accuracy, CluCF employs time factor. Time-aware user-service matrix Mu;s(tk, d) is introduced, and the time-aware similarity measurement and time-aware QoS prediction are employed in this paper. Since the QoS performance of Web services is highly related to invocation time due to some time-varying factors (e.g., service status, network condition), time-aware similarity measurement and time-aware QoS prediction are more trustworthy than traditional similarity measurement and QoS prediction, respectively. Since similarity measurement and QoS prediction are two key steps of neighborhood-based CF, time-aware CF will be more accurate than traditional CF. Moreover, our approach systematically combines user-based and item-based methods and employs influence weights to balance these two predicted values, automatically. To validate our algorithm, this paper conducts a series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that our approach is capable of alleviating the data sparsity problem.     

Towards an ideal service QoS in fuzzy logic-based adaptation planning middleware

Mobile applications require an adaptation phase to adapt to the user's and application context. Utility functions or rules are most often used to make the adaptation planning or decision, i.e. select the most adapted variant for each required service. Fuzzy controllers are used when it is difficult or even impossible to construct precise mathematical models. In the case of mobile applications, the large number of Quality of Service (QoS) and context parameters causes an exponential increase in the number of rules (aka. rule explosion problem), that increases the processing time of the adaptation planning. To reduce the processing time and simplify the fuzzy control system, we propose the concept of ideal QoS. Fuzzy values of ideal QoS parameters are calculated using several fuzzy control systems to fit the context state and user preferences. A fuzzy logic similarity metric based on fuzzy sets and fuzzy operators is proposed to select the service variant having the nearest QoS values to the ideal. Experiments show that our approach can significantly improve both the number of rules and the processing time when selecting the variant that well adapts to environment changes.     

基于协同过滤的自适应Web服务QoS预测方法

随着Web服务越来越多,服务质量QoS作为描述Web服务的非功能性属性变得越来越重要。通常,一种服务的QoS对用户来说是未知的,因此对于基于Web服务的应用,精确预测其未知的QoS对于成功部署该服务具有重要的价值。基于协同过滤的WSRec算法是一种高精度的QoS预测方法,为进一步提升QoS的预测精度,提出了一种协同过滤的自适应Web服务QoS预测方法。该方法通过客户端首先发出QoS-Web服务请求;服务端接到请求后,根据已有数据,计算两两用户或服务间的相似度;并根据相似性找到对于目标用户的K个最接近用户或服务,生成该QoS值预测值A;同时在计算相似性时,采用改进皮尔逊相关系数得到预测值B;最后将预测值A和B以权值相结合得到目标用户或服务的QoS值。该算法改进了单一的协同过滤在数据稀疏的情况下,对相似性给予过高估计的不足,使得QoS预测值精度得以提高,取得了更好的实验结果。实验表明该方法预测精度优于WSRec算法。     

An adaptive quality of service aware middleware for replicated services

A dependable middleware should be able to adaptively share the distributed resources it manages in order to meet diverse application requirements, even when the quality of service (QoS) is degraded due to uncertain variations in load and unanticipated failures. We have addressed this issue in the context of a dependable middleware that adaptively manages replicated servers to deliver a timely and consistent response to time-sensitive client applications. These applications have specific temporal and consistency requirements, and can tolerate a certain degree of relaxed consistency in exchange for better response time. We propose a flexible QoS model that allows clients to specify their timeliness and consistency constraints. We also propose an adaptive framework that dynamically selects replicas to service a client's request based on the prediction made by probabilistic models. These models use the feedback from online performance monitoring of the replicas to provide probabilistic guarantees for meeting a client's QoS specification. The experimental results we have obtained demonstrate the role of feedback and the efficacy of simple analytical models for adaptively sharing the available replicas among the users under different workload scenarios.     

Toward personalized and adaptive QoS assessments via context awareness

Quality of Service (QoS) properties play an important role in distinguishing between functionally equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, using the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been used to a limited extent by existing approaches. In response, we propose a context‐aware quality learning model, realized via a learning‐enabled service agent, exploiting the contextual characteristics of the domain to provide more personalized, accurate, and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.     

Incorporating packet semantics in scheduling of real-time multimedia streaming

In this work, we develop a novel packet scheduling algorithm that properly incorporates the semantics of a packet. We find that improvement in overall packet loss does not necessarily coincide with improvement in user perceivable QoS. The objective of this work is to develop a packet scheduling mechanism which can improve the user perceivable QoS. We do not focus on improving packet loss, delay, or burstiness. We develop a metric called, “Packet Significance,” that effectively quantifies the importance of a packet that properly incorporates the semantics of a packet from the perspective of compression. Packet significance elaborately incorporates inter-frame, intra-frame information dependency, and the transitive information dependency characteristics of modern compression schemes. We apply packet significance in scheduling the packet. In our context, packet scheduling consists of two technical ingredients: packet selection and interval selection. Under limited network bandwidth availability, it is desirable to transmit the subset of the packets rather than transmitting the entire set of packets. We use a greedy approach in selecting packets for transmission and use packet significance as the selection criteria. In determining the transmission interval of a packet, we incorporate the packet significance. Simulation based experiments with eight video clips were performed. We embed the decoding engine in our simulation software and examine the user perceivable QoS (PSNR). We compare the performance of the proposed algorithm with best effort scheduling scheme and one with simple QoS metric based scheduling scheme. Our Significance-Aware Scheduling scheme (SAPS) effectively incorporates the semantics of a packet and delivers best user perceivable QoS. SAPS can result in more packet loss or burstier traffic. Despite these limitations, SAPS successfully improves the overall user perceivable QoS.     

Cloud Service Recommendation Based on a Correlated QoS Ranking Prediction

Quality-of-Service (QoS) is an important concept for service selection and user satisfaction in cloud computing. So far, service recommendation in the cloud is done by means of QoS, ranking and rating techniques. The ranking methods perform much better, when compared with the rating methods. In view of the fact that the ranking methods directly predict QoS rankings as accurately as possible, in most of the ranking methods, an individual QoS value alone is employed to predict the cloud rank. In this paper, we propose a correlated QoS ranking algorithm along with a data smoothing technique and combined with QoS to predict a personalized ranking for service selection by an active user. Experiments are conducted employing a WSDream-QoS dataset, including 300 distributed users and 500 real world web services all over the world. Six different techniques of correlated QoS ranking schemes have been proposed and evaluated. The experimental results showed that this approach improves the accuracy of ranking prediction when compared to a ranking prediction framework using a single QoS parameter.     

IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments

Quality of Service (QoS) value prediction and QoS ranking prediction have their significance in optimal service selection and service composition problems. QoS based service ranking prediction is an NP-Complete problem which examines the order of ranked service sequence with respect to the unique QoS requirements. To address the NP-Complete problem, greedy and optimization-based strategies such as CloudRank and PSO have been widely employed in service oriented environments. However, they pose several challenges with respect to the similarity measure based QoS prediction, trap at local optima, and near optimal solution. Hence, this paper presents Improved Binary Gravitational Search Strategy (IBGSS), an optimization based search strategy to address the challenges in the state-of-the-art QoS value prediction and service ranking prediction techniques. IBGSS employs improved cosine similarity measure, and Newton–Raphson inspired Binary Gravitational Search Algorithm (NR-BGSA) for accurate QoS value prediction and optimal service ranking prediction respectively. The effectiveness of IBGSS over the state-of-the-art QoS value prediction and ranking prediction techniques was validated using two real world QoS datasets, namely WSDream#1 and web service QoS dataset in terms of various statistical measures (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Average Precision Correlation (APC)).     

Web服务聚类方法的研究和改进

Web服务的聚类能够改善基于服务的应用如服务发现、组合和QoS预测等.然而目前的聚类方法在相似度度量和信息预处理方面存在一些不足.提出Web服务的QoS和功能两种相似度模型,从不同角度度量服务间的相似度.在此基础上,提出一种特殊的考虑到编程风格和命名规则的预处理方法.最后结合SCAN算法实现了本方法并设计了对比实验对提出的方法进行验证.实验结果表明提出的模型和方法能够有效地提高Web服务的聚类效果.     

Services recommended trust algorithm based on cloud model attributes weighted clustering

There are many different cloud services available, each with different offerings and standards of quality. Choosing a credible and reliable service has become a key issue. To address the shortcomings of existing evaluation methods, we propose a service clustering method based on weighted cloud model attributes. We calculate user-rating similarity with the weighted Pearson correlation coefficient method based on service clustering, and then compute user similarity combined with the user service selection index weight. This method allows us to determine the nearest neighbors. Finally, we obtain the recommended trust of the service for the target user through the recommendation trust algorithm. Simulation results show that the proposed algorithm can more accurately calculate service recommended trust. This method meets the demand of users in terms of service trust, and it improves the success rate of user service selection.     

Multi-valued collaborative QoS prediction for cloud service via time series analysis

Aiming at the diversity of user features, the uncertainty and the variation characteristics of quality of service (QoS), by exploiting the continuous monitoring data of cloud services, this paper proposes a multi-valued collaborative approach to predict the unknown QoS values via time series analysis for potential users. In this approach, the multi-valued QoS evaluations consisting of single-value data and time series data from consumers are transformed into cloud models, and the differences between potential users and other consumers in every period are measured based on these cloud models. Against the deficiency of existing methods of similarity measurement between cloud models, this paper presents a new vector comparison method combining the orientation similarity and dimension similarity to improve the precision of similarity calculation. The fuzzy analytic hierarchy process method is used to help potential users determine the objective weight of every period, and the neighboring users are selected for the potential user according to their comprehensive similarities of QoS evaluations in multiple periods. By incorporating the multi-valued QoS evaluations with the objective weights among multiple periods, the predicted results can remain consistent with the periodic variations of QoS. Finally, the experiments based on a real-world dataset demonstrate that this approach can provide high accuracy of collaborative QoS prediction for multi-valued evaluations in the cloud computing paradigm.     

面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。