With the growing popularity of web services, more and more developers are composing multiple services into mashups. Developers show an increasing interest in non-popular services (i.e., long-tail ones), however, there are very scarce studies trying to address the long-tail web service recommendation problem. The major challenges for recommending long-tail services accurately include severe sparsity of historical usage data and unsatisfactory quality of description content. In this paper, we propose to build a deep learning framework to address these challenges and perform accurate long-tail recommendations. To tackle the problem of unsatisfactory quality of description content, we use stacked denoising autoencoders (SDAE) to perform feature extraction. Additionally, we impose the usage records in hot services as a regularization of the encoding output of SDAE, to provide feedback to content extraction. To address the sparsity of historical usage data, we learn the patterns of developers' preference instead of modeling individual services. Our experimental results on a real-world dataset demonstrate that, with such joint autoencoder based feature representation and content-usage learning framework, the proposed algorithm outperforms the state-of-the-art baselines significantly.

With the increasing popularity and great economic benefit from vulnerability exploitation, it is important to study mobile vulnerability in the mobile ecosystem. Beyond the traditional technical solutions such as developing technologies to identify potential vulnerabilities, discover the widely available exploitations and protect consumers from attacks, constructing the vulnerability market, a marketplace for vulnerability discovery, disclosure and exploitation, has been considered as an effective approach. Therefore, understanding the mechanism of the vulnerability market for further optimizations is attracting attentions from both academia and industry. Since mobile ecosystem is playing an increasingly important role for the daily life, this paper aims to understand the evolution of the mobile vulnerability market in a data-driven approach, aiming to identify the important issues for further research. Specially, a five-layer heterogeneous network, consisting of the software vendors, products, public disclosed vulnerabilities, hunters, organizations and their relations, is established to formally represent the evolution of the mobile vulnerability market. Based on the data collected from a variety of agencies, including NVD, OSVDB, BID and vendor advisories, a comprehensive empirical analysis is reported, focusing on the growth of the mobile vulnerability market as well as the interactions between mobile and other PCs platforms. Finally, suggestions drawn from the observations, including security evaluation for code reused, data leaking protection and permission overuse identification, hunter's strategy and behavior understanding, information sharing and external workforce hiring, as well as cross-platform vulnerability digging are discussed for further security enhancement.

Latent Dirichlet Allocation(LDA) is a popular topic model. Given the fact that the input corpus of LDA algorithms consists of millions to billions of tokens, the LDA training process is very time-consuming, which prevents the adoption of LDA in many scenarios, e.g., online service. GPUs have benefited modern machine learning algorithms and big data analysis as they can provide high memory bandwidth and tremendous computation power. Therefore, many frameworks, e.g. TensorFlow, Caffe, CNTK, support GPUs for accelerating various data-intensive machine learning algorithms. However, we observe that the performance of existing LDA solutions on GPUs is not satisfying. In this paper, we present CuLDA, a GPU-based efficient and scalable approach to accelerate large-scale LDA problems. CuLDA is designed to efficiently solve LDA problems at high throughput. To this end, we first delicately design workload partitioning and synchronization mechanism to exploit multiple GPUs. Then, we offload the LDA sampling process to each individual GPU by optimizing from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA outperforms them by a large margin (up to 7.3X) on a single GPU. CuLDA is able to achieve an extra 7.5X speedup on 8 GPUs for large data sets.

GPUs have benefited many ML algorithms. However, we observe that the performance of existing Latent Dirichlet Allocation(LDA) solutions on GPUs are not satisfying. We present CuLDA_CGS, an efficient approach to accelerate large-scale LDA problems. We delicately design workload partition and synchronization mechanism to exploit multiple GPUs. We also optimize the algorithm from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA_CGS outperforms them by a large margin (up to 7.3X) on a single GPU.

Service composition remains an important topic where recommendation is widely recognized as a core mechanism. Existing works on service recommendation typically examine either association rules from mashup-service usage records, or latent topics from service descriptions. This paper moves one step further, by studying latent topic models over service collaboration history. A concept of service co-occurrence topic is coined, equipped with a mechanism developed to construct service co-occurrence documents. The key idea is to treat each service as a document and its co-occurring services as the bag of words in that document. Four gauges are constructed to measure self-co-occurrence of a specific service. A theoretical approach, Service Co-occurrence LDA (SeCo-LDA), is developed to extract latent service co-occurrence topics, including representative services and words, temporal strength, and services' impact on topics. Such derived knowledge of topics will help to reveal the trend of service composition, understand collaboration behaviors among services and lead to better service recommendation. To verify the effectiveness and efficiency of our approach, experiments on a real-world data set were conducted. Compared with methods of Apriori, content matching based on service description, and LDA using mashup-service usage records, our experiments show that SeCo-LDA can recommend service composition more effectively, i.e., 5% better in terms of Mean Average Precision than baselines.

Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. While existing approaches and tools focus on facilitating the development of semantic annotations on web services, the validation of the quality of annotations is largely overlooked. Meanwhile, the refinement of semantic annotations mostly goes through manual processes, which not only is time-consuming but also requires significant domain knowledge. To enhance the Quality of Semantic Annotation (QoSA), we have developed a technique to incrementally assess and correct semantic annotations of web services. Aiming at supporting web service interoperation, we have formalized the QoSA of input and output parameters. Based on such formalism, test cases are automatically generated to validate service annotations. Learned semantic instances are then accumulated to iteratively validate semantic annotations of other services. Furthermore, a three-phase optimization methodology including local-feedback, global-feedback, and global-propagate is developed to improve the QoSA by incrementally correcting inaccurate annotations. Experiments over a real-world web services repository have demonstrated that our technique can effectively improve QoSA of services, gaining a 78.68 percent improvement in input parameters annotations and identifying 36.47 percent inaccurate output parameters annotations. The proposed technique can be equipped at various service repositories to enhance service discovery and recommendation.

Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. While many problem-specific optimization techniques have been proposed, alternating least square (ALS) remains popular due to its general applicability (e.g. easy to handle positive-unlabeled inputs), fast convergence and parallelization capability. Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficent. This is because a single machine provides limited compute power for large-scale data while multiple machines suffer from the network communication bottleneck.

To improve the efficiency of elderly assessments, an influence-based fast preceding questionnaire model (FPQM) is proposed. Compared with traditional assessments, the FPQM optimizes questionnaires by reordering their attributes. The values of low-ranking attributes can be predicted by the values of the high-ranking attributes. Therefore, the number of attributes can be reduced without redesigning the questionnaires. A new function for calculating the influence of the attributes is proposed based on probability theory. Reordering and reducing algorithms are given based on the attributes’ influences. The model is verified through a practical application. The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90.56% with a prediction accuracy of 98.39%. Compared with other methods, such as the Expert Knowledge, Rough Set and C4.5 methods, the FPQM achieves the best performance. In addition, the FPQM can also be applied to other questionnaires.

Sharing economy refers to peer-based activities of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services. It is known as collaborative consumption that people share the services rather than having individual ownership. By leveraging idle resources to produce more goods and services, sharing economy significantly drives green consumption and sustainable development in our human society. Using information technology to provide individuals with information enables the optimization of resources through the mutualization of excess capacity in goods and services. A common premise is that when information is shared, the value of the goods may increase for businesses, for individuals, for communities, and for the whole society in general. Currently, sharing economy has potentially resulted in a great impact on citizens’ everyday life and generated huge economic benefits, e.g., Airbnb, Uber, and Amazon Mechanical Turk. A host of enabling technologies has reached the mainstream for the rise of sharing economy, including open data, the ubiquity of low-cost mobile phones, and social media. These technologies dramatically reduce the friction of share-based business and organizational models.

Web services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component services from a sea of service candidates is often challenging. Therefore, web service recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by service providers. In this paper, we propose a novel way of dynamically reconstructing objective service profiles based on mashup descriptions, which carry historical information of how services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of services and bridge the vocabulary gap between mashup developers and service providers. Specifically, we jointly model mashup descriptions and component service using author topic model in order to reconstruct service profiles. Exploiting word features derived from the reconstructed service profiles, a new service recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed service recommendation algorithm is effective and outperforms the state-of-the-art methods.

Nowadays many companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a data center provider to maximize its revenue by intelligently scheduling tasks in software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, thus may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. The core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. Comparing with state-of-the-art methods, the experimental results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-trip time of tasks of all applications.

Stochastic gradient descent (SGD) is widely used by many machine learning algorithms. It is efficient for big data ap- plications due to its low algorithmic complexity. SGD is inherently serial and its parallelization is not trivial. How to parallelize SGD on many-core architectures (e.g. GPUs) for high efficiency is a big challenge. In this paper, we present cuMF_SGD, a parallelized SGD solution for matrix factorization on GPUs. We first design high-performance GPU computation kernels that accelerate individual SGD updates by exploiting model parallelism. We then design efficient schemes that parallelize SGD updates by exploiting data parallelism. Finally, we scale cuMF SGD to large data sets that cannot fit into one GPU's memory. Evaluations on three public data sets show that cuMF_SGD outperforms existing solutions, including a 64- node CPU system, by a large margin using only one GPU card.

With the rapidly increasing number of services, there is an urgent demand for service recommendation algorithms that help to automatically create mashups. However, most traditional recommendation algorithms rely on the original service descriptions given by service providers. It is detrimental to the recommendation performance because original service descriptions often lack comprehensiveness and pertinence in describing possible application scenarios, let alone the possible language gap existing between service providers and mashup developers. To solve the above issues, a novel method of Targeted Reconstructing Service Descriptions (TRSD) for a specific mashup query is proposed, resorting to the valuable information hidden in mashup descriptions. TRSD aims at introducing mashup descriptions into service descriptions by analyzing the similarity between existing mashups and the specific query, while leveraging service system structure information. Benefit from this approach, missing application scenarios in original service descriptions, query-specific application scenario information, mashup developers' language habits, and service system structure information are all integrated into the reconstructed service descriptions. Based on the reconstructed service description by TRSD, a new service recommendation strategy is developed. Comprehensive experiments on the real-world data set from ProgrammableWeb.com show that the overall MAP of the proposed TRSD model is 6.5% better than the state-of-the-art methods.

Service recommendation plays a critical role in fostering the growth of service ecosystems. However, existing methods are mainly in favor of a small number of popular services while newly emerged ones (i.e., newborn services) are largely ignored, which hurts the systems in two aspects. First, the potential of many services, especially the newborn ones, is wasted. Second, service ecosystems highly depending on a few kernel services are not diversified nor robust. To address this issue, we propose to proactively recommend collaborative services for newborn ones. The aim is to illuminate how to use the newborn services and fertilize their proper usages. While this is a cold start problem, frequent collaboration among newborn or dissimilar services makes it more difficult. In this situation, a Divide-and-Conquer approach is adopted utilizing category tags and collaboration records (DCCC). For each newborn service, the approach first produces one ranked list of old services and one list of newborn services, separately. DCCC then merges the two lists into one for recommendation. Experiments over a real-world dataset from ProgrammableWeb demonstrate that the proposed approach achieves significant improvement in recommendation accuracy compared with baseline methods.

Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges. The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells. Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance (even with standard RNN cells) in tasks involving very long-term dependencies. To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures. We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures. The code for our method is publicly available at https://github.com/code-terminator/DilatedRNN.

Summary For service‐based process, there is a frequent failure when it runs under a loose‐coupled and uncertain environment, such as mobile Internet. The runtime self‐adaptation mechanism is urgently needed to automatically achieve the goal of a process. Therefore, this article presents a self‐adaptation framework to automatically monitor and self‐adapt to various failures. On the basis of the presented self‐adaptation exceptions framework, the service‐based process is automatically extended with these exception definitions. In particular, the local self‐adaptation exceptions monitor the failures over each web service instance, whereas the global self‐adaptation exceptions are designed to explore the process‐level failures. For each exception, the self‐adaptation strategy is dynamically generated according to the running environment. Finally, we implement the prototype system running on the Apache ODE. The experimental results show that our framework can automatically recover a process instance from various failures, and the proposed approach can identify more failure types than previous works and apparently improve the running success rate of process instances.

A key factor of win–win cloud economy is how to trade off between the application performance from customers and the profit of cloud providers. Current researches on cloud resource allocation do not sufficiently address the issues of minimizing energy cost and maximizing revenue for various applications running in virtualized cloud data centers (VCDCs). This paper presents a new approach to optimize the profit of VCDC based on the service-level agreements (SLAs) between service providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines at different service classes. An analytic probabilistic model is then developed for non-steady VCDC states. In addition, a smart controller is developed for fine-grained resource provisioning and sharing among multiple applications. Furthermore, a novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. The advantage of the proposed approach is validated with trace-driven simulations.

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.

As cloud computing is becoming growingly popular, consumers' tasks around the world arrive in cloud data centers. A private cloud provider aims to achieve profit maximization by intelligently scheduling tasks while guaranteeing the service delay bound of delay-tolerant tasks. However, the aperiodicity of arrival tasks brings a challenging problem of how to dynamically schedule all arrival tasks given the fact that the capacity of a private cloud provider is limited. Previous works usually provide an admission control to intelligently refuse some of arrival tasks. Nevertheless, this will decrease the throughput of a private cloud, and cause revenue loss. This paper studies the problem of how to maximize the profit of a private cloud in hybrid clouds while guaranteeing the service delay bound of delay-tolerant tasks. We propose a profit maximization algorithm (PMA) to discover the temporal variation of prices in hybrid clouds. The temporal task scheduling provided by PMA can dynamically schedule all arrival tasks to execute in private and public clouds. The sub problem in each iteration of PMA is solved by the proposed hybrid heuristic optimization algorithm, simulated annealing particle swarm optimization (SAPSO). Besides, SAPSO is compared with existing baseline algorithms. Extensive simulation experiments demonstrate that the proposed method can greatly increase the throughput and the profit of a private cloud while guaranteeing the service delay bound.

The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

Matrix factorization (MF) is used by many popular algorithms such as collaborative filtering. GPU with massive cores and high memory bandwidth sheds light on accelerating MF much further when appropriately exploiting its architectural characteristics.

With the booming number of web services, it is a challenge for inexperienced developers to select suitable services and make service compositions. Therefore, recommending services based on user queries becomes a necessity. For modeling the queries and services' descriptions, many recent studies are based on LDA (Latent Dirichlet Allocation). However, some previous empirical works indicate that LDA model doesn't gain high accuracy in generating latent presentation which is subject to the restrictive assumption of the Dirichlet-Multinomial distribution. In this paper, we propose a Time-aware Collaborative Poisson Factorization (TCPF) to tackle the problem. TCPF takes Poisson Factorization as the foundation to model mashup queries and service descriptions separately, and incorporate them with the historical usage data together using collective matrix factorization. Experiments on the real-world ProgrammableWeb dataset show that our model outperforms the state-of-the-art methods (e.g., Time-aware collaborative domain regression) by 7.7% in terms of mean average precision, and costs much less time on the sparse, massive and long-tailed data set.

Service ecosystem consists of all kinds of services, and some of them may be composed by developers to create new mashups. Existing work on service recommendation and composition mine either frequent patterns from mashup-service usage records, or latent topics from service metadata. In this paper, we propose Service Co-occurrence LDA (SeCo-LDA), a novel approach that mines latent topic models over service co-occurrence patterns. The key idea is to treat each service as a document, and its bag of co-occurring services as the bag of words in that document. Using this model, we can analyze such service co-occurrence documents with a probabilistic topic model. We show how to derive service co-occurrence topics, and then validate our model on the real-world ProgrammableWeb.com dataset. We illustrate that SeCo-LDA can discover meaningful latent service composition patterns including their temporal strength and services' impacts, which conventional Apriori can not reveal. Comparing with Apriori, content matching based on service description and LDA directly using mashup-service usage records, we have demonstrated that SeCo-LDA can recommend service composition more effectively, 5% better in terms of MAP than the baseline approach.

Currently, most web service recommendation studies concentrate on mining association patterns among services from historical compositions and recommending proper services based on patterns derived. However, latent negative patterns which indicate the inappropriate combinations of services, are mostly ignored. Therefore, by combining additional negative patterns with the already-exploited positive patterns in the large spares network of web services, we present a more comprehensive and accurate model for service recommendation. More specifically, we combine positive and negative composition patterns mined from service annotated tags. The extensive experiments conducted on a real-life dataset show that our method can outperform not only traditional APriori -based recommendation method but also Link Prediction-based one. The experiments on a synthetic dataset show that our method can also be effective to make recommendations in large-scale service network.

Multiple heterogeneous applications concurrently run in distributed cloud data centers (CDCs) for better performance and lower cost. There is a highly challenging problem of how to minimize the total cost of a CDCs provider in a market where the bandwidth and energy cost show geographical diversity. To solve the problem, this paper first proposes a revenue-based workload admission control method to judiciously admit requests by considering factors including priority, revenue and the expected response time. Then, this paper presents a cost-aware workload scheduling method to jointly optimize the number of active servers in each CDC, and the selection of Internet service providers for the CDCs provider. Finally, trace-driven simulation results demonstrate that the proposed methods can greatly reduce the total cost and increase the throughput of the CDCs provider in comparison to existing methods. Note to Practitioners-A cloud provider deploys its applications in geographically distributed CDCs to improve stability and reliability. For cost and performance, each CDC provides services through multiple ISPs that deliver traffic between millions of users and the CDCs provider. The geographical diversity of the bandwidth and energy cost brings the CDCs provider a big challenge of how to minimize the bandwidth and energy cost of the CDCs provider. This paper first proposes a revenue-based workload admission control method to selectively admit requests. Then, this paper proposes a cost-aware workload scheduling method to allocate requests among multiple available Internet service providers connecting to distributed CDCs. The scheduling strategy can intelligently dispatch requests, and achieve lower cost and higher throughput for the CDCs provider.

The balance between customer-perceived application performance and cloud provider's profit is a key to achieve win-win in cloud economy. Current researches on cloud resource allocation do not sufficiently address the issue of minimizing energy cost and maximizing revenue for various applications in virtualized cloud data center (VCDC). This paper presents a new approach to realize the optimization of VCDC's profit based on the service-level agreements between cloud providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines of different service classes. An analytic probabilistic model is then developed for non-equilibrium VCDC states. Next, a smart controller is proposed for fine-grained resource provisioning and sharing among multiple applications. A novel hybrid meta-heuristic algorithm based on simulated annealing and particle swarm optimization is developed to solve the formulated profit maximization problem. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Finally, the effectiveness of the proposed approach is validated with trace-driven simulation.

The recent shift towards write-intensive workload on big data (e.g., financial trading, social user-generated data streams)has pushed the proliferation of log-structured key-value stores, represented by Google's BigTable [1], Apache HBase [2] andCassandra [3]. While providing key-based data access with aPut/Get interface, these key-value stores do not support value-based access methods, which significantly limits their applicability in modern web and database applications. In this paper, we present DELI, a DEferred Lightweight Indexing scheme on the log-structured key-value stores. To index intensively updated bigdata in real time, DELI aims at making the index maintenance as lightweight as possible. The key idea is to apply an append-only design for online index maintenance and to collect index garbage at carefully chosen time. DELI optimizes the performance of index garbage collection through tightly coupling its execution with a native routine process called compaction. The DELI's system design is fault-tolerant and generic (to most key-valuestores), we implemented a prototype of DELI based on HBase without internal code modification. Our experiments show that the DELI offers significant performance advantage for the write-intensive index maintenance.

Mash up has emerged as a promising way to compose web APIs and create value-added compositions. The increasing of APIs demands more accurate recommendation algorithms. However, service domain evolution, mash up-side cold-start and information evaporation are somehow overlooked by existing work. In this paper, by extending the collaborative topic regression (CTR) model, the procedure of service selection is modeled with a generative process, and the mash up-side cold-start problem that cannot be dealt with by naïve CTR is resolved. By learning the maximum a posteriori estimates of the whole generative process, both content information and historical usage are taken into consideration to extract service domains, thus the service domains can evolve with the evaluation of historical usage pattern. Meanwhile, information evaporation is also considered by giving time-related confidence levels to historical usage to track the evolution of service ecosystem. Experiments on the real-world Programmable Web data set show that compared with the state-of-the-art methods, our approach gains a 6.8% improvement in terms of recommendation accuracy.

Semantic annotation plays an important role for semantic-aware web service discovery, recommendation and composition. In recent years, many approaches and tools have emerged to assist in semantic annotation creation and analysis. However, the Quality of Semantic Annotation (QoSA) is largely overlooked despite of its significant impact on the effectiveness of semantic-aware solutions. Moreover, improving the QoSA is time-consuming and requires significant domain knowledge. Therefore, how to verify and improve the QoSA has become a critical issue for semantic web services. In order to facilitate this process, this paper presents a novel lifecycle framework aiming at QoSA assessment and optimization. The QoSA is formally defined as the success rate of web service invocations, associated with a verification framework. Based on a local instance repository constructed from the execution information of the invocations, a two-layer optimization method including a local-feedback strategy and a global-feedback one is proposed to improve the QoSA. Experiments on real-world web services show that our framework can gain 65.95%~148.16% improvement in QoSA, compared with the original annotation without optimization.

Recently, the mediation-aided approach is attracting more attention in Web service composition, in the meanwhile, temporal constraints are regarded as an important aspect to ensure the correctness and QoS in service compositions. This combination leads to a new challenge in analyzing the timed compatibility of mediation-aided service composition. Unfortunately, existing model checking based approaches are lack of the ability of transform mediation-aided service composition to Time Automata (TA) models, and suffer from state space explosion for large-scale and complex compositions. In this paper, we present a new model checking approach to analyzing timed compatibility. Firstly, mediation-aided service composition is automatically decomposed into fragments. Secondly, each fragment is transformed into a TA. Finally, the temporal constraints are checked by the queries of observing TAs. Compared with existing approaches, our approach is able to check timed compatibility of mediation-aided service composition, and is more efficient than them.

Content-Centric Networking (CCN) has recently emerged as a clean-slate Future Internet architecture which has a completely different communication pattern compared with exiting IP network. Since the World Wide Web has become one of the most popular and important applications on the Internet, how to effectively support the dominant browser and server based web applications is a key to the success of CCN. However, the existing web browsers and servers are mainly designed for the HTTP protocol over TCP/IP networks and cannot directly support CCN-based web applications. Existing research mainly focuses on plug-in or proxy/gateway approaches at client and server sides, and these schemes seriously impact the service performance due to multiple protocol conversions. To address above problems, we designed and implemented a CCN web browser and a CCN web server to natively support CCN protocol. To facilitate the smooth evolution from IP networks to CCN, CCNBrowser and CCNxTomcat also support the HTTP protocol besides the CCN. Experimental results show that CCNBrowser and CCNxTomcat outperform existing implementations. Finally, a real CCN-based web application is deployed on a CCN experimental testbed, which validates the applicability of CCNBrowser and CCNxTomcat.

Summary Service‐oriented computing and cloud computing are playing critical roles in supporting business collaboration over the Internet. Thanks to the latest development in computing technologies, various large‐scale, evolving, and rapidly growing service ecosystems emerge. However, service failures greatly hamper the usability and reputation of service ecosystems. In the previous work, service failure is not adequately studied from an ecosystem's perspective. To address this gap, we propose a service failure analysis framework based on a complex network model of service ecosystem. This framework comprises a feature model of failed services and several service failure impact indicators. By applying the framework, empirical analysis of failed service features and failure impact assessment can be implemented more easily and precisely. Moreover, to provide failure tolerance strategies for service ecosystems, a novel composition‐based service substitution method is designed to replace the failed services with functional similar ones, such that the service systems are more robust when a failure occurs. As the new substitution method requires fewer structural data of services, it is more convenient to be applied in present RESTful Representational State Transfer (REST) service environment. Both the framework and the service substitution method are tested on real‐world data set, and their usability and efficiency are demonstrated. Copyright © 2014 John Wiley & Sons, Ltd.

Chatting with nearby interested strangers instantly in location-based mobile social network (LMSN) has become increasingly popular. Currently, friend recommendation relies only on the simple and limited user profiles, and is agnostic to users' offline behaviors in the real world. For the first time, we focus on utilizing the user's check-in behaviors in the real world, instead of the general acquaintance-based social circles, to instantly recommend nearby strangers to make friends. However, bridging nearby strangers with similar check-in behaviors instantly has some new characteristics, such as lack of common friends and interaction histories, temporal, spatial and user three-dimensional correlation, and sparseness of check-ins. Most existing work about friend recommendations mainly focuses on making friends within the acquaintance-based social circles, and has not fully considered these new characteristics mentioned above. Therefore, how to catch the ephemeral opportunity to recommend nearby interested strangers instantly remains a challenge. In this paper, we present to use “Encounter” probability to measure the behavior similarity of two strangers in the real world based on their check-in histories. To address the sparseness challenge of check-in data, a Kernel Density Estimation (KDE)-based user check-in probability estimation method considering the spatiotemporal dimensions is proposed to estimate each user's check-in probability distribution with time at each spot. Finally, we use a large-scale user check-in dataset of Gowalla to validate the effectiveness of this approach. The experimental results show that our approach outperforms other commonly used similarity computation methods.

Analyzing time series data can reveal the temporal behavior of the underlying mechanism producing the data. Time series motifs, which are similar subsequences or frequently occurring patterns, have significant meanings for researchers especially in medical domain. With the fast growth of time series data, traditional methods for motif discovery are inefficient and not applicable to large-scale data. This work proposes an efficientMotifDiscovery method forLarge-scaletimeseries (MDLats). By computing standard motifs, MDLats eliminates a majority of redundant computation in the related arts and reuses existing information to the maximum. All the motif types and subsequences are generated for subsequent analysis and classification. Our system is implemented on a Hadoop platform and deployed in a hospital for clinical electrocardiography classification. The experiments on real-world healthcare data show that MDLats outperform the state-of-the-art methods even in large time series.

It is complex and difficult to perform the vehicle scheduling of urban bus lines, which is important to reduce the operational cost and improve the quality of public transportation services. One has to assign vehicles to cover a set of trips contained in a timetable while minimizing multiple objectives that may conflict with each other. Existing approaches combine these objectives in a weighted fashion to form a single objective and then use a single-objective optimization approach to solve it. However, they can only produce one solution, and it is not easy to assign a proper weight for each objective to obtain a superior solution that can balance different objectives. In this paper, a methodology is presented to create a set of Pareto solutions for this problem. First, a set of candidate vehicle blocks is generated. Then, multiple block subsets are selected from this candidate set by an improved multiobjective genetic algorithm combined with a departure-time adjustment procedure to obtain multiple Pareto solutions. To encode a solution, we propose a coding scheme that has a relatively short coding length and low decoding complexity. This approach is applied to a real-world vehicle scheduling problem of a bus line in Nanjing, China. Experiments show that this approach is able to quickly produce satisfactory Pareto solutions that outperform the actually used experience-based solution.

Mashup has emeraged as a promising way to allow developers to compose existed APIs (services) to create new or value-added services. With the rapid increasing number of services published on the Internet, service recommendation for automatic mashup creation gains a lot of momentum. Since mashup inherently requires services with different functions, the recommendation result should contain services from various categories. However, most existing recommendation approaches only rank all candidate services in a single list, which has two deficiencies. First, ranking services without considering to which categories they belong may lead to meaningless service ranking and affect the recommendation accuracy. Second, mashup developers are not always clear about which service categories they need and services in which categories cooperate better for mashup creation. Without explicitly recommending which service categories are relevant for mashup creation, it remains difficult for mashup developers to select proper services in a mixed ranking list, which lower the user friendliness of recommendation. To overcome these deficiencies, a novel category-aware service clustering and distributed recommending method is proposed for automatic mashup creation. First, a Kmeans variant(vKmeans) method based on topic model Latent Dirichlet Allocation is introduced for enhancing service categorization and providing a basis for recommendation. Second, on top of vKmeans, a service category relevance ranking (SCRR) model, which combines machine learning and collaborative filtering, is developed to decompose mashup requirements and explicitly predict relevant service categories. Finally, a category-aware distributed service recommendation (CDSR) model, which is based on a distributed machine learning framework, is developed for predicting service ranking order within each category. Experiments on a real-world dataset have proved that the proposed approach not only gains significant improvement at precision rate but also enhances the diversity of recommendation results.

In the paradigm of service oriented science, scientific computing applications and data are all wrapped as web accessible services. Scientific workflows further integrate these services to answer complex research questions. However, our earlier study conducted on myExperiment has revealed that although the sharing of service-based capabilities opens a gateway to resource reuse, in practice, the degree of reuse is very low. This finding has motivated us to propose ServiceMap to provide navigation facility through the network of services to facilitate the design and development of scientific workflows. This paper proposes ReputationNet as an enhancement of ServiceMap, to incorporate the often-ignored reputation aspects of services/workflows and their publishers, in order to offer better service and workflow recommendations. We have developed a novel model to reflect the reputation of e-Science services/workflows, and developed heuristic algorithms to provide service recommendations based on reputations. Experiments on myExperiment have illustrated a strong positive correlation (with Pearson correlation coefficient 0.82) between the reputation scores computed and the actual performance (i.e. usage frequency) of the services/workflows, which demonstrates the effectiveness of our approach.

Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques, while others are based on QoS measurement. However, service ecosystem is evolving over time with services publishing, prospering and perishing. Few existing methods consider or exploit the evolution of service ecosystem on service recommendation. This paper employs a probabilistic approach to predict the popularity of services to enhance the recommendation performance. A method is presented that extracts service evolution patterns by exploiting latent dirichlet allocation (LDA) and time series prediction. A time-aware service recommendation framework is established for mashup creation that conducts joint analysis of temporal information, content description and historical mashup-service usage in an evolving service ecosystem. Experiments on a real-world service repository, ProgrammableWeb.com, show that the proposed approach leads to a higher precision than traditional collaborative filtering and content matching methods, by taking into account temporal information.

Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the utilization of many graph algorithms in Big Data scenarios. To address the performance issues in large scale graph analytics, we develop a graph processing system called System G, which explores efficient graph data organization for parallel computing architectures. We discuss various graph data organizations and their impact on data locality during graph traversals, which results in various cache performance behavior on processor side. In addition, we analyze data parallelism from architecture's perspective and experimentally show the efficiency for System G based graph analytics. We present experimental results for commodity multicore clusters and IBM PERCS supercomputers to illustrate the performance of System G for large scale graph analytics.

Log-Structured-Merge (LSM) Tree gains much attention re-cently because of its superior performance in write-intensive workloads. LSM Tree uses an append-only structure in memory to achieve low write latency; at memory capac-ity, in-memory data are flushed to other storage media (e.g. disk). Consequently, read access is slower comparing to write. These specific features of LSM, including no in-place update and asymmetric read/write performance raise unique challenges in index maintenance for LSM. The structural difference between LSM and B-Tree also prevents mature B-Tree based approaches from being directly applied. To address the issues of index maintenance for LSM, we pro-pose Diff-Index to support a spectrum of index maintenance schemes to suit different objectives in index consistency and performance. The schemes consist of sync-full, sync-insert, async-simple and async-session. Experiments on our HBase implementation quantitatively demonstrate that Diff-Index offers various performance/consistency balance and satisfac-tory scalability while avoiding global coordination. Sync-insert and async-simple can reduce 60%-80 % of the overall index update latency when compared to the baseline sync-full; async-simple can achieve superior index update per-formance with an acceptable inconsistency. Diff-Index ex-ploits LSM features such as versioning and the flush-compact process to achieve goals of concurrency control and failure

Big data poses challenges to the technologies required to process data of high volume, velocity, variety, and veracity. Among the challenges, the storage and computing required by big data analytics is usually huge, and as a result big data capabilities are often provisioned in cloud and delivered in the form of Web-based services. Solid-state drive (SSD) is widely used nowadays as an elementary hardware feature in cloud infrastructure for big data services. For example, Amazon Web Service (AWS) offers EC2 instances with SSD storage, and its key-value data store, DynamoDB, is backed up by SSD for superior performance. Compared to hard disk drive (HDD), SSD prevails in both access latency and bandwidth. In the foreseeable future, SSD would be readily available on commodity servers though its capacity would be neither large enough nor cost effective to accommodate big data on its own. Therefore, it is essential to investigate how to efficiently leverage SSD as one layer in a storage hierarchy in addition to HDD. In this paper, we investigate the effectiveness of using SSD in three workloads, namely standalone Hadoop MapReduce jobs, Hive jobs, and HBase queries. Firstly, we device an approach to enable Hadoop Distributed File System (HDFS) having a SSD-HDD storage hierarchy. Secondly, we investigate the IO involved in different phases of Hadoop jobs and design different schemes to place data discriminatively in the aforementioned storage hierarchy. Afterward, the effectiveness of different schemes are evaluated with respect to job run time. Finally, we summarize best practices of data placement for examined workloads in a SSD-HDD storage hierarchy.

Service compositions inherently require multiple services each with its domain-specific functionality. Therefore, how to mine matching patterns between services in relevant domains and compositions becomes crucial to service recommendation for composition. Existing methods usually overlook domain relevance and domain-specific matching patterns, which restrict the quality of recommendations. In this paper, a novel approach is proposed to offer domain-aware service recommendation. First, a K Nearest Neighbor variant (vKNN) based on topic model Latent Dirichlet Allocation (LDA) is introduced to cluster services into semantically coherent domains. On top of service domain clustering results by vKNN, a probabilistic matching model Domain Router (DR) based on Extreme Learning Machine (ELM) is developed for decomposing a requirement to relevant domains. Finally, a comprehensive Domain Topic Matching (DTM) model is built to mine relevant domain-specific matching patterns to facilitate service recommendation. Experiments on a large-scale real-world dataset show that DTM not only gains significant improvement at precision rate but also enhances the diversity of results.

Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques such as keyword search and semantic matching while others are based on Quality of Service (QoS) prediction. However, services and their mashups are evolving over time with publishing, perishing and changing of interfaces. Therefore, a practical service recommendation approach should take into account the evolution of a service ecosystem. In this paper, we present a method to extract service evolution patterns by exploiting Latent Dirichlet Allocation (LDA) and time series prediction. A time-aware service recommendation framework for mashup creation is presented combing service evolution, collaborative filtering and content matching. Experiments on real-world ProgrammableWeb data set show that our approach leads to a higher precision than traditional collaborative filtering and content matching methods.

Quality-of-Service (QoS)-aware web service composition is of great importance to assemble individual services into a composite one meeting functional and nonfunctional requirements. Given a large number of candidate services, automatic composition is essential so as to derive a composite service efficiently. Most existing methods return one solution that is optimal in some given criteria. This is somewhat rigid in terms of flexibility. In case some component service in the optimal composition becomes unavailable, the composition algorithm has to run again to find another optimal solution. Also, in a lot of circumstances users prefer multiple alternatives over a single one. Therefore, providing top- k service compositions according to their QoS is becoming more desirable. On another aspect, from the perspective of computation efficiency, due to the explosion of the searching space, single-threaded methods are usually not capable of handling a large number of candidate services. This paper tackles these two issues together, i.e., large-scale, QoS-based services composition yielding top- k solutions. The composition algorithm is based on the combination of backtrack search and depth-first search, which can be executed in a parallel way. Experiments are carried out based on the datasets provided by the WS-Challenge competition 2009 and China Web Service 2011. The results show that our approach can not only find the same optimal solution as the winning systems from these competitions, but also provide alternative solutions together with the optimal QoS.

Recently, the temporal constraint satisfiability is regarded as an important criterion in Web service composition to guarantee its timely completion. This leads to a new challenge in analyzing the compatibility of Web services under temporal constraints. The existing methods either do not consider message mismatches between services in a composition or suffer from state-space explosion by verifying a service composition model as a whole; or lack the ability to generate execution paths of each participating service. In this paper, we present a Petri net-based method to address these three issues in a holistic manner, and also in a modular way. Compared with the existing work, the proposed approach not only composes Web services by adding a mediation net to deal with message mismatches, but also checks the compatibility w.r.t. temporal constraints by generating modular timed state graphs. Furthermore, the reliable and usable execution paths that satisfy the timed compatibility can be derived to guide service execution and avoid any temporal exception.

Service computing plays a critical role in business automation and we can observe a rapid increase of web services and their compositions nowadays. Web services, their compositions, providers, consumers, and other entities such as context information, collectively form an evolving service ecosystem. Many service recommendation methods have been proposed to facilitate the use of services. However, existing approaches are mostly based on all-time statistics of usage patterns, and overlook the temporal aspect, i.e., the evolution of the ecosystem. As a result, recommendation may consist of obsolete services and also does not reflect the latest trend in the ecosystem. In order to overcome this limitation, we propose an innovative three-phase network prediction approach (NPA) for evolution-aware recommendation. First, we introduce a network series model to formalize the evolution of the service ecosystem and then develop a network analysis method to study the usage pattern with a special focus on its temporal evolution. Afterward a novel service network prediction method based on rank aggregation is proposed to predict the evolution of the network. Finally, using the network prediction model, we present how to recommend potential compositions, top services and service chains, respectively. Experiments on the real-world ProgrammableWeb data set show that our method achieves a superior performance in service recommendation, compared with those that are agnostic to the evolution of a service ecosystem.

Cigarette production scheduling is vital in improving production efficiency and reducing supply delay. In this paper, a workflow model combined with an immune algorithm is proposed to solve this problem for improving production efficiency. First, the problem is formulated as a mixed-integer quadratically constrained programming model. Afterwards, the problem is transformed into a workflow resource allocation one. Based on this model, an immune algorithm is presented to find a set of activity priorities that are combined with dispatching rules to allocate resources. Activity priorities are represented by antibodies and evaluated by simulation runs on the workflow model. The proposed approach is applied to several production scheduling instances, and results are compared with other approaches. Experiments show that the result from the proposed approach is substantially better than those obtained from other approaches. It is demonstrated that our approach can effectively reduce production tardiness and improve efficiency.

Public clouds provide Infrastructure as a Service (IaaS) to users who do not own sufficient compute resources. IaaS achieves the economy of scale by multiplexing, and therefore faces the challenge of scheduling tasks to meet the peak demand while preserving Quality-of-Service (QoS). Previous studies proposed proactive machine purchasing or cloud federation to resolve this problem. However, the former is not economic and the latter for now is hardly feasible in practice. In this paper, we propose a resource allocation framework in which an IaaS provider can outsource its tasks to External Clouds (ECs) when its own resources are not sufficient to meet the demand. This architecture does not require any formal inter-cloud agreement that is necessary for the cloud federation. The key issue is how to allocate users' tasks to maximize the profit of IaaS provider while guaranteeing QoS. This problem is formulated as an integer programming (IP) model, and solved by a self-adaptive learning particle swarm optimization (SLPSO)-based scheduling approach. In SLPSO, four updating strategies are used to adaptively update the velocity of each particle to ensure its diversity and robustness. Experiments show that, SLPSO can improve a cloud provider's profit by 0.25%-11.56% compared with standard PSO; and by 2.37%-16.71% for problems of nontrivial size compared with CPLEX under reasonable computation time.

Services computing is playing a critical role in recent years in many fields and we observe a rapidly increasing number of web accessible services and their compositions nowadays. However, our earlier empirical study reveals that, overall the public available services are under-utilized, and when they are used, they are used mostly in an isolated manner. This phenomenon inspires us to further explore a methodology to help consumers understand the usage pattern of the service ecosystem, including interactions among services, and the evolution of these interactions. Based on the derived usage pattern, this methodology also introduces a service recommendation method that suggests both services and their compositions, in a time-sensitive manner. We firstly construct an evolution network model from the historical usage of the services in the ecosystem. Then a rank-aggregation-based link prediction method is proposed to predict the evolution of the ecosystem. Based on this link prediction method, we can recommend services and compositions of interest to service developers. Through an experiment on the real-world mashup-service ecosystem, i.e., Programmable Web, we demonstrated that our approach can effectively recommend services and compositions with better precision than the methods we compared.

The emergence of Web services has changed the Internet a lot, and greatly facilitated the development of service based software systems. How to select appropriate services and compose them according to given context to satisfy a user's requirement is a big challenge. This paper proposes a novel Genetic Algorithm (GA) method to synthesis web services in a context-aware environment. We first present a context space model to illustrate both contexts and services in a formal way, we utilize GA to compose context-aware services according to users' preference. We transform the problem of service composition to a multi-objective optimization problem. To resolve the conflict and dependencies among services in GA process, we propose a service similarity tree (SST) model to measure the similarity between services. Finally, we design a simulation experiment to evaluate our method. The experiment result shows that our method is a promising one to solve service composition problem in a context-aware environment.

In a context-aware pervasive environment, it is crucial to provide an efficient adaption mechanism to rapidly discover appropriate services and compose them to achieve user desired situation when context change. In this paper, we propose a GA(Genetic Algorithm) based novel service composition method in a context-aware environment. We first use Context Space model to represent context, situation, user preference and services in a unified formal way. Then we transform the service composition problem into a multi-objective optimization problem and utilize GA to find the optimal service composition based on user's preference. To affiliate the connection between context and services, we use a vector to represent each service to reflect which context types a service can change so as to utilize GA to find the best service composition. Confliction of services may happen during the GA process. In order to resolve the conflicts, we propose a SST(Service Similarity Tree) method to measure the similarity among services to find out the best alternative. Finally, we design and implement a simulation experiment to verify our method. The results shows that our method can leverage GA to synthesis appropriate services to achieve user desired goal in an efficient way.

SUMMARY As enterprises turning to SOA, services‐oriented business ecosystem (SOBE) has become an important pattern for the organization and management of the massive business services. At the same time, the emergence of Internet of Services (IOS) provides a business model in which service vendors and consumers can interact with each other via the Internet. This paradigm makes it possible that the services in SOBE are managed in an autonomous and coordinated manner. The challenge here is to organize these massive business services, coordinate, and federate them to achieve the benefits of SOA. To address these challenges, this paper presentsBSNet, a framework on the basis of the service correlation networks to manage the SOBE. The model consists of awho‐what‐howservice correlation network that captures the various relations in SOBE. Finally, a prototype system is developed, and a simulated case study is provided to show the expanded value of our network‐based framework. Copyright © 2013 John Wiley & Sons, Ltd.

A service ecosystem consists of services and their compositions (i.e., mashups) and evolves as a complex network system. It is driven by continuously emerged new services and the mashups of old services and new ones. Complex network analysis can be a powerful tool to study the static structure as well as the evolution of a service ecosystem. This paper presents a methodology to study such a system and an empirical study of Programmable Web. To the best of our knowledge, Programmable Web is the largest and most active Web APIs and mashups collection and consists of 4337 services and 6092 service compositions by Nov-2011. We conduct a comprehensive network analysis to quantitatively characterize the static structure and dynamic evolution of the ecosystem. The findings of this paper not only can help understand the current usage pattern and the evolution trace of the ecosystem, but also are applicable to other Web service systems.

The increasing number of cores integrated into modern processors is blurring the line between supercomputers and enterprise-grade servers. Therefore, the same attention to lock contention bottlenecks must be given to Java-based business workloads as it is given to massively parallel, high-performance computing applications, especially when it comes to characterizing global trends that would ease the transition of today's code base to tomorrow's parallel configurations. This paper first presents the characteristics of a typical Java-based business application software stack and examines the locking contentions that can appear at each level of that stack. Second, it presents scalability evaluation of three enterprise-grade, Java-based workloads and details the lock contention founds. Third, it summarizes the results of our findings, emphasizing the need for a streamlined methodology for lock-contention analysis of enterprise Java workloads.

Performance of data streaming applications is co-determined by both networking and computing resources, and therefore they should be co-scheduled and co-allocated in an integrated and coordinated way. Dynamic control of resource scheduling and allocation is required, because unilateral redundancy in either networking or computing resources may result in the overprovision of it and the other may become a bottleneck. To avoid resource shortage as well as overprovision, in this paper, a virtualized platform is utilized to implement data streaming and processing. In this platform, fuzzy logic controllers are designed to allocate CPU resources; iterative bandwidth allocation is applied and is processing- and storage-aware to guarantee on-demand data provisioning. Experimental results show that our approach leads to higher application performance as well as higher resource utilization, compared with other resource scheduling and allocation methods.

With the advancement of enterprise information system, services-oriented business ecosystem (SOBE) has became an important pattern for the organization and management of the massive business services. At the same time, Internet of Service (IOS) provides a business model in which service vendors and consumers can interactive with each other via the Internet. This paradigm makes it possible that services in SOBE be managed in an autonomous and coordinated manner. The challenge here is to really coordinate the distributed services and federate them to achieve the benefits of SOA. To address these shortcomings, this paper presents "BSNet", a formal model of the SOBE. The model consists of a three-layer business service correlation network which describes the relationships in SOBE. In fact, it is a "who-what-how" correlation network construction of SOBE. Furthermore the expanded value of this model to business service management is shown. Finally, a simulation-base case study using BSNet to organize and manage SOBE is presented.

caGrid has accumulated a repository of biomedical services, however, how a cancer researcher can find proper services in the caGrid when needed remains a big challenge. This research aims to enhance the cyber infrastructure of caGrid, by developing a mechanism that turns caGrid services into semantic-aware interoperable services. We proposed a service semantics model, and developed a technique that automatically extracts semantic metadata from static WSDL service descriptions. Such semantic information is stored as loosely coupled annotations that can be queried using semantic Web techniques, to enhance services discovery and composition. We also proposed a two-phase discovery technique that helps users quickly identify interested service operations. This paper also reports our examinations over available techniques and recommends a feasible infrastructure for biomedical service reuse. A prototyping system is developed as a proof of concept.

BACKGROUND: In biological and medical domain, the use of web services made the data and computation functionality accessible in a unified manner, which helped automate the data pipeline that was previously performed manually. Workflow technology is widely used in the orchestration of multiple services to facilitate in-silico research. Cancer Biomedical Informatics Grid (caBIG) is an information network enabling the sharing of cancer research related resources and caGrid is its underlying service-based computation infrastructure. CaBIG requires that services are composed and orchestrated in a given sequence to realize data pipelines, which are often called scientific workflows. RESULTS: CaGrid selected Taverna as its workflow execution system of choice due to its integration with web service technology and support for a wide range of web services, plug-in architecture to cater for easy integration of third party extensions, etc. The caGrid Workflow Toolkit (or the toolkit for short), an extension to the Taverna workflow system, is designed and implemented to ease building and running caGrid workflows. It provides users with support for various phases in using workflows: service discovery, composition and orchestration, data access, and secure service invocation, which have been identified by the caGrid community as challenging in a multi-institutional and cross-discipline domain. CONCLUSIONS: By extending the Taverna Workbench, caGrid Workflow Toolkit provided a comprehensive solution to compose and coordinate services in caGrid, which would otherwise remain isolated and disconnected from each other. Using it users can access more than 140 services and are offered with a rich set of features including discovery of data and analytical services, query and transfer of data, security protections for service invocations, state management in service interactions, and sharing of workflows, experiences and best practices. The proposed solution is general enough to be applicable and reusable within other service-computing infrastructures that leverage similar technology stack.

With the emergence of "service oriented science," the need arises to orchestrate multiple services to facilitate scientific investigation-that is, to create "science workflows." We present here our findings in providing a workflow solution for the caGrid service-based grid infrastructure. We choose BPEL and Taverna as candidates, and compare their usability in the lifecycle of a scientific workflow, including workflow composition, execution, and result analysis. Our experience shows that BPEL as an imperative language offers a comprehensive set of modeling primitives for workflows of all flavors; while Taverna offers a dataflow model and a more compact set of primitives that facilitates dataflow modeling and pipelined execution. We hope that this comparison study not only helps researchers select a language or tool that meets their specific needs, but also offers some insight on how a workflow language and tool can fulfill the requirement of the scientific community.

Under Service Oriented Architecture (SOA), service composition is used to integrate service components together to meet new business needs. In this paper, we propose a novel data-driven method to provide service composition guidance to implement given requirements. Based on the relations between business domain data and service domain data, we generate additional data mediations according to three composition rules. With these data relations and composition rules, we propose a Petri-net based approach to the composition of services. In our approach, all the in/output messages of the service operations are modeled as colored places, and service operations themselves are modeled as transitions with input/output places. We first generate a Service Net (SN) that contains all operations in a given service portfolio, and then use Petri-net decomposition techniques to derive a subnet of SN, and this subnet meets the need of the business requirement. Our work can be seen as an effort to bridge the gap between business and service domains.

Web service models are increasingly being used in the Grid community as way to create distributed applications exposing data and/or applications through self describing interfaces. Scientific research is one key field in which the benefits are apparent as individual services can be orchestrated into experimental workflows that model the research process and facilitate verification and extension. However, many applications are not web enabled and the task of creating services from scratch is cumbersome in part due to the range of complex technologies, tools, standards and languages involved. In this paper we present gRAVI, a WSRF Web service wrapping tool that allows scientists to rapidly expose applications, scripts and workflows as Web services. gRAVI generated services include GSI security, Grid scheduling, state notifications, persistence and data staging. All service code, scripts and definition files are created automatically without any developer input. gRAVI services are created in standard Grid Archive files and are able to be moved and deployed to any compliant container with no requirement for any gRAVI or Grid infrastructure on the target machine. gRAVI supports deployment to the open science cloud Nimbus, whilst also being able to parse Taverna workflow definition files to create strongly typed services.

In scientific collaboration platforms such as caGrid, workflow-as-a-service is a useful concept for various reasons, such as easy reuse of workflows, access to remote resources, security concerns, and improved execution performance. We propose a solution for facilitating workflow-as-a-service based on Taverna as the workflow engine and gRAVI as a service wrapping tool. We provide both a generic service to execute all Taverna workflows, and an easy-to-use tool (gRAVI-t) for users to wrap their workflows as workflow-specific services, without developing service code. The signature of the specific service is identical to the corresponding workflow's input/output definition and is therefore more self-explained to workflow users. These two categories of services are useful in different scenarios, respectively. We use a tumor analysis workflow as an example to demonstrate how the workflow-as-a-service approach benefits the execution performance. Finally a conclusion is drawn and future research opportunities are discussed.

Automatic Web Service composition is gaining momentum as the potentialsilver bulletin Service Oriented Architecture. The need for interservice compatibility analysis and indirect composition has gone beyond what the existing service composition/verification technologies can handle. Given two services whose interface invocation constraints are described by a Web Services-Business Process Execution Language (WS-BPEL or BPEL), we analyze their compatibility and adopt mediation as a lightweight approach to make them compatible without changing their internal logic. We first transform a BPEL description into a service workflow net, which is a kind of colored Petri net (CPN). Based on this formalism, we analyze the compatibility of two services, and then devise an approach to check whether there exists any message mediator so that their composition does not violate the constraints imposed by either side. The method for mediator generation is finally proposed to assist the automatic composition of partially compatible services. Our approach is validated through a real-life case and further research directions are pointed out.

In the above titled paper (ibid., vol. 6, no. 1, pp. 94-106, Jan 09), Fig. 1 was incorrect. The correct figure is presented here.

Scientific communities are increasingly exposing information and tools as online services in an effort to abstract complex scientific processes and large data sets. Clients are then able to access services without knowledge of their internal workings therefore simplifying the process of replicating scientific research. Taking a service-oriented approach to science (SOS) facilitates reuse, extension, and scalability of components, whist also making information and tools available to a wider audience. Scientific workflows play a key role in realizing SOS by orchestrating services into well formed logical pipelines created to model the requirements of complex scientific experiments. The task of developing such service-oriented infrastructures is not trivial as developers must create and deploy Web services and then coordinate multiple services into workflows. This paper presents an end-to-end approach for developing SOS-based workflows with the aim of simplifying development, deployment, and execution. In particular, we use gRAVI to wrap applications as WSRF Web services and Taverna to compose and execute workflows. The process is validated through the creation of a real world bioinformatics workflow involving multiple services and complex execution paths.

caBIGtrade (the cancer Biomedical Informatics Gridtrade) is an open-source, open-access information network enabling cancer researchers to share tools, data, applications, and technologies. caGrid is the underlying service-based grid software infrastructure for caBIG, integrating distributed data and analytic resources into a virtual collaborative platform for cancer research. Within caGrid, many cancer-related data analysis and aggregation tasks can make use of "canned" sets of service invocations, or workflows. As a result, there is a need to orchestrate the invocation of caGrid services through the use of both a workflow language and tooling. In this paper, we first explain why we select Taverna as a candidate for workflow authoring and invocation. We then review the development of Taverna plug-ins in general, and describe how we extend Taverna to use caGrid services. We then detail a real-world example and the lessons learned from our research. Finally we conclude with a summary and a description of potential next steps.

Abstract The emergence of service‐oriented architecture (SOA) has brought about a loosely coupled computing environment that enables flexible integration and reuse of heterogeneous systems. On building a SOA for application systems, more and more research has been focused on service composition, in which workflow and simulation techniques have shown great potential. Simulation of services' interaction is important since the services ecosystem is dynamic and in continuous evolution. However, there is a lack in the research of services' simulation, especially models, methods and systems to support the simulation of interaction behavior of composite services. In this paper, an enhanced workflow simulation method with the support of interactive events mechanism is proposed to fulfill this requirement. At build time, we introduce an event sub‐model in the workflow meta‐model, and our simulation engine supports the event‐based interaction pattern at run time. With an example simulated in the prototype system developed according to our method, the advantages of our method in model verification and QoS evaluation for service compositions are also highlighted. Copyright © 2007 John Wiley & Sons, Ltd.

Solution design has been more of an art than an engineering discipline. Lots of researchers and practitioners have proposed and exercised different kinds of approaches with varied success. Most of these methods seem to have focused on building new solutions from scratch. However, enterprise solutions today are mostly built on top of an existing IT infrastructure. The notion of SoA is trying to pave a way to integrate heterogeneous components together to meet new business needs. When a new requirement is given to a system developer in the form of business processes, it would be ideal if she/he can make the best of existing services for many reasons. In this paper we propose a data driven approach to provide service composition guidance to implement the given requirement. Based on the relations among business domain data and service domain data, we can generate additional data mediations according to three composition rules. With these data relations and composition rules, we give a formal approach to devise choreography of services from current service portfolio, plus additional data mediation artifacts to realize a given requirement. Our work can be seen as an effort to bridge the gap between business and service domain