Upper confidence bound (UCB)-based contextual bandit algorithms require one to know the tail property of the reward distribution. Unfortunately, such tail property is usually unknown or difficult to specify in real-world applications. Using a tail property heavier than the ground truth leads to a slow learning speed of the contextual bandit algorithm, while using a lighter one may cause the algorithm to diverge. To address this fundamental problem, we develop an estimator (evaluated from historical rewards) for the contextual bandit UCB based on the multiplier bootstrap technique. Our proposed estimator mitigates the problem of specifying a heavier tail property by adaptively converging to the ground truth contextual bandit UCB (i.e., eliminating the impact of the specified heavier tail property) with theoretical guarantees on the convergence. The design and convergence analysis of the proposed estimator is technically nontrivial. The proposed estimator is generic and it can be applied to improve a variety of UCB-based contextual bandit algorithms. To demonstrate the versatility of the proposed estimator, we apply it to improve the linear reward contextual bandit UCB (LinUCB) algorithm resulting in our bootstrapping LinUCB (BootLinUCB) algorithm. We prove that the BootLinUCB has a sublinear regret. We conduct extensive experiments on both synthetic dataset and real-world dataset from Yahoo! to validate the benefits of our proposed estimator in reducing regret and the superior performance of BootLinUCB over the latest baseline.

Hierarchical clustering is an important research branch of cluster analysis that has extensive ranges of practical applications. Meanwhile, it still faces problems such as inaccurate, time-consuming, and difficulty in choosing linkage method. In this paper, we present a new Hierarchical Clustering method based on Local Cores and Sharing concept (HCLCS) which takes a "divide-and-merge" framework by first dividing a data set into several small clusters and then merging them hierarchically. To improve the accuracy, the merging process is further divided into two substeps: (1) pre-connect small clusters that belong very likely to the same category, and (2) merge the pre-connected intermediate clusters and the remaining unconnected small clusters in a classical hierarchical way. Extensive experiments on synthetic and real-world data sets show that HCLCS can achieve better performance than existing methods in dealing with data sets with complex structures and is less time-consuming than two state-of-the-art algorithms (SNN-DPC and RSC).

Online rating system serves as an indispensable building block for many web applications such as Amazon, TripAdvior and Yelp. It enables production quality estimation via aggregate ratings (a.k.a. wisdom of the crowd) as well as product recommendation via inferring user preference from ratings, etc. Previous studies showed that due to assimilate-contrast effects, historical ratings can significantly distort user's ratings, leading to low accuracy of product quality estimation and recommendation. To understand assimilate-contrast effects, an "accurate'' model is still missing as previous models do not capture important factors like rating recency, selection bias, etc. Furthermore, an analytical framework to characterize product estimation accuracy under assimilate-contrast effects is also missing. This paper aims to fill in this gap. We propose a mathematical model to quantify the aforementioned important factors on assimilate-contrast effects. Our model attains a good balance between model complexity and model accuracy, such that it is neat enough for us to develop an analytical framework to study assimilate-contrast effects. Based on our model, we derive sufficient conditions, under which the product estimation and collective opinion converges to the "ground-truth''. These conditions reveal important insights on how the aforementioned factors influence the convergence and guide the online rating system operator to design appropriate rating aggregation rules and rating displaying strategies. To demonstrate the versatility of our model, we apply to rating prediction tasks and product recommendation tasks. Experiment results on four public datasets show that our model can improve the rating prediction and and recommendation accuracy over previous models significantly.

Clustering analysis has been widely used in statistics, machine learning, pattern recognition, image processing, and so on. It is a great challenge for most existing clustering algorithms to discover clusters with arbitrary shapes. Clustering algorithms based on Minimum spanning tree (MST) are able to discover clusters with arbitrary shapes, but they are time consuming and susceptible to noise points. In this paper, we employ local density peaks (LDP) to represent the whole data set and define a shared neighbors-based distance between local density peaks to better measure the dissimilarity between objects on manifold data. On the basis of local density peaks and the new distance, we propose a novel MST-based clustering algorithm called LDP-MST. It first uses local density peaks to construct MST and then repeatedly cuts the longest edge until a given number of clusters are found. The experimental results on synthetic data sets and real data sets show that our algorithm is competent with state-of-the-art methods when discovering clusters with complex structures.

Cloud computing has nowadays become a dominant technology to reduce the computation cost by elastically providing resources to users on a pay-per-use basis. More and more scientific and business applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, efficient cloud workflow scheduling methods are in high demand. This paper investigates how to simultaneously optimize makespan and economical cost for workflow scheduling in clouds and proposes a multiobjective evolutionary list scheduling (MOELS) algorithm to address it. It embeds the classic list scheduling into a powerful multiobjective evolutionary algorithm (MOEA): a genome is represented by a scheduling sequence and a preference weight and is interpreted to a scheduling solution via a specifically designed list scheduling heuristic, and the genomes in the population are evolved through tailored genetic operators. The simulation experiments with the real-world data show that MOELS outperforms some state-of-the-art methods as it can always achieve a higher hypervolume (HV) value. Note to Practitioners-This paper describes a novel method called MOELS for minimizing both costs and makespan when deploying a workflow into a cloud datacenter. MOELS seamlessly combines a list scheduling heuristic and an evolutionary algorithm to have complementary advantages. It is compared with two state-of-the-art algorithms MOHEFT (multiobjective heterogeneous earliest finish time) and EMS-C (evolutionary multiobjective scheduling for cloud) in the simulation experiments. The results show that the average hypervolume value from MOELS is 3.42% higher than that of MOHEFT, and 2.27% higher than that of EMS-C. The runtime that MOELS requires rises moderately as a workflow size increases.

It is critical to evaluate the quality of clusters for most cluster analysis. A number of cluster validity indexes have been proposed, such as the Silhouette and Davies-Bouldin indexes. However, these validity indexes cannot be used to process clusters with arbitrary shapes. Some researchers employ graph-based distance to cluster nonspherical data sets, but the computation of graph-based distances between all pairs of points in a data set is time-consuming. A potential solution is to select some representative points. Inspired by this idea, we propose a novel Local Cores-based Cluster Validity (LCCV) index to improve the performance of Silhouette index. Local cores, with local maximum density, are selected as representative points. Since graph-based distance is used to evaluate the dissimilarity between local cores, the LCCV index is effective for obtaining the optimal cluster number for data sets containing clusters with arbitrary shapes. Moreover, a hierarchical clustering algorithm based on the LCCV index is proposed. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.

Accurately estimating unknown quality-of-service (QoS) data based on historical records of Web-service invocations is vital for automatic service selection. This work presents an effective scheme for addressing this issue via alternating direction method-based matrix factorization. Its main idea consists of a) adopting the principle of the alternating direction method to decompose the task of building a matrix factorization-based QoS-estimator into small subtasks, where each one trains a subset of desired parameters based on the latest status of the whole parameter set; b) building an ensemble of diversified single models with sophisticated diversifying and aggregating mechanism; and c) parallelizing the construction process of the ensemble to drastically reduce the time cost. Experimental results on two industrial QoS datasets demonstrate that with the proposed scheme, more accurate QoS estimates can be achieved than its peers with comparable computing time with the help of its practical parallelization.

Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.

Hierarchical clustering is of great importance in data analysis. Although there are a number of hierarchical clustering algorithms including agglomerative methods, divisive methods and hybrid methods, most of them are sensitive to noise points, suffer from high computational cost and cannot effectively discover clusters with complex structures. When recognizing patterns from complex structures, humans intuitively tend to discover obvious clusters in dense regions firstly and then deal with objects on the border. Inspired by this idea, we propose a local cores-based hierarchical clustering algorithm called HCLORE. The proposed method first partitions the data set into several clusters by finding local cores, instead of optimizing an objective function through iteration like K-means; then, temporarily removes points with lower local density, so that the boundary between clusters is clearer; after that, merges clusters according to a new defined similarities between clusters; and finally, points with lower local density are assigned to the same clusters as their local cores belong to. The experimental results on synthetic data sets and real data sets show that our algorithm is more effective and efficient than existing methods when processing data sets with complex structures.

As a kind of network security protection technology, intrusion detection technology has become one of the hot topics in the field of network security. In order to solve the problem that the methods of network anomaly detection have a high requirement on the purity of the normal data-set, and that the existing methods based on outlier detection need to set an anomaly threshold manually. Combining with the idea of Natural Neighborhood Graph, a network anomaly detection method (NAD-NNG) is proposed. In order to eliminate noise points or mislabel points and reduce the time complexity of anomalies detection, the algorithm uses the Natural Neighborhood Graph to cluster the normal data-set. Also, the algorithm can adaptively obtain a percentage value β for setting the anomaly threshold. Experiments on KDDCUP99 show that compared with the other two algorithms, the proposed method can achieve a higher detection rate based on a tolerable false alarm rate.

When a single service on its own cannot fulfill a sophisticated application, a composition of services is required. Existing methods mostly use a fixed-price scheme for service pricing and determine service allocation for composition based on a first-price auction. However, in a dynamic service market, it is difficult for service providers to determine a fixed price that is profitable while attractive to customers. Meanwhile, this mechanism cannot ensure that the providers who require the least cost to provide services would win the auction, because the pricing strategy of service providers is unpredictable. To address such issues, in this paper, we propose Vickrey-Clarke-Groves auction-based dynamic pricing for a generalized service composition. We consider fine-grained services as candidates for composition as well as coarse-grained ones. In our approach, service providers bid for services of different granularities in the composite service and based on received bids, a user decides a composition that minimizes the social cost while meeting quality constraints. Experimental results at last verify the feasibility and effectiveness of the proposed approach.

Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector. Thus, the second-order information is innovatively integrated into them. Experimental results on two industrial QoS datasets indicate that compared with the state-of-the-art predictors, the newly proposed one achieves significantly higher prediction accuracy at the expense of affordable computational burden. Hence, it is especially suitable for industrial applications requiring high prediction accuracy of unknown QoS data.

Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.

Clustering analysis has been widely used in many areas such as astronomy, bioinformatics, and pattern recognition. In 2014, Rodriguez proposed an algorithm based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher density. But the density relies on cutoff distance, which might be affected by large statistical error, and the algorithm does not suit the clustering problem of multi-scale data. In this paper, a new neighbor concept Natural Neighbor is proposed. Natural neighbor-based density, is simple and well reflects the data distribution without any parameters. Then, we extend each cluster from its center by searching natural neighbors of points in this cluster, and we define extension rules to determine the cluster boundary. The experiment results show our algorithm is more effective on multi-scale data.

Cluster analysis is aimed at classifying elements into categories on the basis of their similarity.And cluster analysis has been widely used in many areas such as pattern recognition, and image processing.In this paper, we propose an approach based on the idea that the density of cluster centers are highest in its k nearest neighborhood or reverse k nearest neighborhood, and clusters is divided by sparse region.We firstly define the similarity between clusters.Based on this idea, no matter non-spherical data or complex manifold data, the proposed algorithm is applicable.And the proposed algorithm has a certain capacity on outliers detection.We demonstrate the power of the proposed algorithm on several test cases.Its clustering performance is better than DBSCAN, DP and K-AP clustering algorithms.

Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain high prediction accuracy and scalability. Most current MF-based models, however, are static ones that cannot adapt to incremental user feedbacks. This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders. With it, a recommender is designed to consist of two components, i.e., a static one built on static rating data, and an incremental one built on a sub-matrix related to rating-variations only. Highly reliable predictions are thus generated by fusing their results. The experiments on large industrial datasets show that desired accuracy and acceptable computational complexity are achieved by the resulting recommender with the proposed scheme.

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and storage complexity, as well as slow convergence rate, which prevents them from industrial usage in context of big data. To address these issues, this paper proposes an alternating direction method (ADM)-based nonnegative latent factor (ANLF) model. The main idea is to implement the ADM-based optimization with regard to each single feature, to obtain high convergence rate as well as low complexity. Both computational and storage costs of ANLF are linear with the size of given data in the target matrix, which ensures high efficiency when dealing with extremely sparse matrices usually seen in CF problems. As demonstrated by the experiments on large, real data sets, ANLF also ensures fast convergence and high prediction accuracy, as well as the maintenance of nonnegativity constraints. Moreover, it is simple and easy to implement for real applications of learning systems.

Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the fulfillment of nonnegativity constraints can better represent the positive value nature of QoS data, thereby boosting the prediction accuracy and 2) since QoS prediction is a learning task, it is promising to further improve the prediction accuracy with a carefully designed ensemble model. To achieve this, we first implement an NLF model for QoS prediction. This model is then diversified through feature sampling and randomness injection to form a diversified NLF model, based on which an ensemble is built. Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.

Quality of service (QoS)-aware optimal service composition aims to maximize the overall QoS value of the resulting composite service instance while meeting user-specified global QoS constraints. Traditional methods only consider as candidates service instances that implement one abstract service in the composite service and neglect those that could perform multiple abstract services. To overcome this shortcoming, this paper proposes the concept of generalized component services (GCSs), which is defined in a semantic manner, to expand the selection scope so as to achieve a better solution. A QoS-aware multigranularity service composition model is formulated and how to identify all the GCSs for a composite service is elaborated. A backtracking-based algorithm and an extended genetic algorithm are proposed to optimize the resulting composite service instance. Lastly, evaluation results of these algorithms are described.

Latent-factor-based Quality-of-Service predictors can achieve high prediction accuracy and good scalability. However, most of them are based on first-order models that cannot well deal with their target problem that is inherently non-convex. Since second-order approaches have proven to be effective to such problems, this work proposes to implement a second-order predictor with an aim to achieve the high accuracy unlikely obtained by any existing methods. To do so, this work adopts the principle of Hessian-free optimization and successfully avoids the usage of a Hessian matrix by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector. Experimental results on two industrial QoS datasets indicate that the newly proposed predictor is highly accurate with fine computational efficiency.

SUMMARY Web services are emerging as a major technology for deploying automated interactions between distributed and heterogeneous applications. The accurate prediction of their quality of service (QoS) is important because their users rely on it to decide whether they meet the QoS requirement. The existing studies of QoS prediction usually assume that QoS of service activities follows certain distributions. These distributions are used as static model inputs into stochastic process models to obtain analytical QoS results. Instead, we consider the QoS activities to be fluctuating and introduce a dynamic framework to predict the runtime QoS by employing an Autoregressive Moving Average Model and QoS reduction rules. In the case study of a real‐world composite service sample, a comparison between existing approaches and the proposed one is presented, and results suggest that the proposed one achieves higher prediction accuracy.Copyright © 2014 John Wiley & Sons, Ltd.

Outlier detection is an important task in data mining because outliers may bring either new knowledge or potential threats. Much of recent research has focused on measuring the local difference between an outlier and its nearest neighbors, some of wh

Cloud computing is a recently developed new technology for complex systems with massive service sharing, which is different from the resource sharing of the grid computing systems. In a cloud environment, service requests from users go through numerous provider-specific steps from the instant it is submitted to when the requested service is fully delivered. Quality modeling and analysis of clouds are not easy tasks because of the complexity of the automated provisioning mechanism and dynamically changing cloud environment. This work proposes an analytical model-based approach for quality evaluation of Infrastructure-as-a-Service cloud by considering expected request completion time, rejection probability, and system overhead rate as key quality metrics. It also features with the modeling of different warm-up and cool-down strategies of machines and the ability to identify the optimal balance between system overhead and performance. To validate the correctness of the proposed model, we obtain simulative quality-of-service (QoS) data and conduct a confidence interval analysis. The result can be used to help design and optimize industrial cloud computing systems.

Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work proposes to build a new LF-based CF model via second-order optimization to achieve higher accuracy. We first investigate a Hessian-free optimization framework, and employ its principle to avoid direct usage of the Hessian matrix by computing its product with an arbitrary vector. We then propose the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process. Compared with LF models based on first-order optimization algorithms, experimental results on two industrial datasets show that the proposed one can offer higher prediction accuracy with reasonable computational efficiency. Hence, it is a promising model for implementing high-performance recommenders.

Cloud computing is a promising paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Virtualization allows to instantiate virtual machines (VMs) on top of fewer physical systems managed by a VM manager. Performance evaluation of clouds is required to evaluate and quantify the cost-benefit of a strategy portfolio and the quality of service (QoS) experienced by end-users. Such evaluation is not feasible by means of simulation or on-the-field measurement, due to the great scale of parameter spaces that have to be traversed. In this study, we present a stochastic-queuing-network-based approach to performance analysis of migration-enabled clouds in error-prone environment. Several performance metrics are defined and evaluated: utilization, expected task completion time, and task rejection rate under different load conditions and error intensities. To validate the proposed approach, we obtain experimental performance data through a real-world cloud and conduct a confidence-interval analysis. The analysis results suggest the perfect coverage of theoretical performance results by corresponding experimental confidence intervals.

With the increasing call for green cloud, reducing energy consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. Dynamic voltage scaling (DVS) has been a key technique in exploiting the hardware characteristics of cloud datacenters to save energy by lowering the supply voltage and operating frequency. This paper presents a novel stochastic framework for energy efficiency and performance analysis of DVS-enabled cloud. This framework uses virtual machine request arrival rate, failure rate, repair rate, and service rate of datacenter servers as model inputs. Based on a queuing-network-based analysis, this paper gives analytic solutions of three metrics. The proposed framework can be used to help the design and optimization of energy-aware high performance cloud systems.

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.

Ontology Web Language for Services (OWL-S), one of the most significant semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of services in an unambiguous computer-interpretable form. Analysis of the quality of service of composite service processes specified in OWL-S enables service users to decide whether the process meets nonfunctional requirements. In this paper, we propose a probabilistic approach for reliability analysis of OWL-S processes, employing the non-Markovian stochastic Petri net (NMSPN) as the fundamental model. Based on the NMSPN representations of the OWL-S elements, we introduce an analytical method for the calculation of the process-normal-completion probability as the reliability estimate. This method takes the probabilistic parameters of service invocations and messages as model inputs. To validate the feasibility and accuracy of our approach, we obtain runtime experimental data and conduct a confidence interval analysis in a case study. A sensitivity analysis is also performed to determine the impact of model parameters on reliability and to help identify the reliability bottlenecks.

As an application of cloud storage, cloud backup is effective, reliable, extensible, cost-effective and usable, which makes cloud backup having a broad prospect of application. We proposed a reliable cloud backup scheduling algorithm that showing good performance. In this algorithm, data access time and data reliability are introduced. With the cloud state table and cloud resources table, the algorithm determines which cloud storage node will save the data. The simulated experiment shows that the proposed algorithm, guaranteeing the data reliability, gets good data access performance.

Business Process Execution Language (BPEL) is considered as the de facto standard for Web service composition. To analyze the performance of composite service processes specified in BPEL gives the way to tell whether the process meets the performance requirements. In this paper, we propose a translation-based approach for performance analysis of BPEL processes, which employs a general stochastic Petri net (GSPN) as the intermediate representation. A set of translation rules is defined for constructs and activities of BPEL so that the processes specified in BPEL can be translated into the GSPN representations. Based on the GSPN representation of BPEL processes, we introduce a state-space method to calculate the expected-process-normal-completion-time as the performance estimate. In the case study, we obtain experimental data and conduct a confidence interval analysis to validate the feasibility and accuracy of the translation-based approach.

OWL-S, one of the most significant Semantic web service ontologies proposed to date, provides Web Service providers with a core ontological framework and guidelines for describing the properties and capabilities of their web Services in unambiguous, computer interpretable form. In this paper, we propose a probabilistic model for OWL-S processes, employing the Non-markovian stochastic Petri net(NMSPN) as the intermediate representation. A set of translation rules is defined for constructs and activities of OWL-S so that processes specified in OWL-S can be translated into NMSPN. Based on the NMSPN representation, analytical methods for QoS analysis can be designed.

Abstract Web Service Choreography Description Language (WS‐CDL) is a mainstream standard for the description of peer‐to‐peer collaborations for the participants of service composition. To predict the dependability of composite service processes specified in WS‐CDL allows service designers and user to decided whether the process meets the nonfunction requirements of trustworthiness, and to choose the process with better dependability to those with identical function. Unfortunately, very limited research attention is paid to dependability of WS‐CDL. In this paper, we propose a model‐driven approach for dependability prediction of composite service built on WS‐CDL. The main innovation of this research includes a complete translation from WS‐CDL to generalized stochastic Petri nets (GSPN) and a dependability (using process‐normal‐completion‐probability as the metric of dependability of service composition) calculation algorithm based on GSPN. We also validate the accuracy of the approach in the experimental study by showing 95% confidence intervals obtained from experimental dependability results that cover the corresponding theoretical prediction values. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Quality of service (QoS) of workflows and workflow‐based applications is given increasing attention by both industry and academic. In this paper, we propose a novel analytical framework to analyze QoS (metrics include make‐span, cost, and reliability) of workflow systems based on GWF‐net, which extends traditional workflow net by associating tasks with generally distributed firing delay and time‐to‐failure. The GFW‐net model is used to model process structure and task organization of workflows at the process level. In contrast with prevailing QoS models based on Markovian process, we introduce a reduction technique to evaluate QoS of GWF‐net process avoiding the state‐explosion problem and tedious mathematical derivation of state‐transition probabilities. Through a case study, we show that our framework is capable of modeling real‐world workflow‐based application effectively. Also, experiments and confidence‐interval analysis in the case study indicate that the reduction methods are verified by real results. We also compare our approach with related research in the text. Copyright © 2008 John Wiley & Sons, Ltd.