With the widespread adoption of cloud-based services and Service-Oriented Computing, efficient service recommendation has become pivotal for optimizing service discovery and composition in large-scale ecosystems. While recent diffusion-based recommendation methods have achieved impressive results in service-oriented scenarios, existing approaches predominantly treat user-service interactions as isolated events, overlooking the potential of higher-order collaborative signals between users and services. Such signals, which encapsulate richer and more nuanced relationships, can be naturally captured using graph-based data structures. To address this limitation, we extend diffusion-based service recommendation methods to the graph domain by directly modeling user-service bipartite graphs with diffusion models. This enables better modeling of the higher-order connectivity inherent in complex interaction dynamics. However, this extension introduces two primary challenges: (1) Noise Heterogeneity, where interactions are influenced by various forms of continuous and discrete noise, and (2) Relation Explosion, referring to the high computational costs of processing large-scale graphs. To tackle these challenges, we propose a Graph-based Diffusion Model for Service Recommendation (GDMSR). To address noise heterogeneity, we introduce a multi-level noise corruption mechanism that integrates both continuous and discrete noise, effectively simulating real-world interaction complexities. To mitigate relation explosion, we design a user-active guided diffusion process that selectively focuses on the high-value edges and active users, reducing inference costs while preserving critical service-level dependencies. Extensive experiments on six real-world service datasets demonstrate that GDMSR consistently outperforms state-of-the-art methods, highlighting its effectiveness in capturing higher-order collaborative signals and improving service recommendation performance.

Sequential recommender systems (SRSs) aim to predict the subsequent content in which users may be interested based on their past usage history. Existing solutions on SRSs focus on modeling sequential characteristics of user-service interactions and achieve promising performance. However, they do not take full advantage of one key factor that usually influences user behaviors: the category of services. It is necessary yet challenging to leverage category information due to two significant reasons. Firstly, bundling relationships exist between services/categories, which is vital for the prediction of user behaviors but hard to mine and encode. Secondly, since interest preferences and category preferences are closely related, their dynamic evolution has to be studied simultaneously. To tackle the above challenges, we propose a novel Dual-channel Preference Extraction and Refinement Network (DPERN) to extract users' multi-faceted preferences toward more accurate recommendation. For the former challenge, we leverage the co-occurrence information of services and categories to represent their intrinsic relationships and then adopt the graph embedding method to jointly pre-train their embeddings. For the latter challenge, we design dual preference extractors, each leveraging both service and category information, to capture interest preferences and category preferences, respectively. Moreover, we devise a preference refinement network to model the interaction between two extracted preferences, to enhance preference representations. Experimental results on three public datasets have demonstrated the effectiveness of the proposed DPERN model.

Driven by Service-Oriented Computing, time-aware service recommendation aims to support personalized mashup development, adapting to the rapid shifts of users’ dynamic preferences. Recently, users’ social connections have shown significant benefits to time-aware service recommendation, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm always presumes a given social network, which is not necessarily consistent with the similarities of service preferences among users and is expensive to collect for most service platforms. We propose a novel idea to learn the graph structure among historical mashups and make time-aware service recommendation for dynamic mashup creation collectively in a coupled framework. This idea raises two challenges, i.e., scalability and accuracy. To solve both challenges simultaneously, we introduce the Dynamic Relation Graph Learning (DRGL) framework for time-aware service recommendation. For scalability, our framework has a coarse-to-fine recalling strategy to learn the graph structure among the mashups, which enables the exploration of potential links among all historical mashups while maintaining a tractable amount of computation. For accuracy, we leverage recent advances in self-attention mechanisms to the mashup modeling and propose a transformer-based mashup encoder, which considers long-range dependencies in dense mashups for more accurate mashup representations. Extensive experiments show that the DRGL model consistently outperforms the state-of-the-art methods in terms of prediction accuracy for mashup creation.

As the adoption of Service-Oriented Computing continues to grow, the number of web services has increased significantly, which makes service recommendation become an essential tool to assist users in selecting suitable services. However, a single service cannot satisfy the complex requirements of users, which has led to the emergence of a new technique known as Mashup, which combines services as reusable components to create value-added service compositions. Along with mashup, mashup recommendation has also become an indispensable and important component of service platforms. On service platforms, there are many heterogeneous entities and complex relationships between them. We divide these interaction into three different views: Mashup-Invocation view, Service-Consumption view, and Mashup-Composition view. As user preferences and characteristics of services and mashups are distributed across different views, their cooperation is crucial for accurate mashup recommendation. Therefore, we propose Cross-view Graph Alignment (CGA), a framework that captures the collaborative associations dispersed across different views and enhances the representation learning of users and mashups. This the first study to jointly tackle structure- and representation-level collaboration on the service platforms for better mashup recommendation. Experiments on two real-world service datasets show that CGA outperforms state-of-the-art methods and can better improve the mashup recommendation.

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.

With the flourishing development of Everything-as-a-Service (EaaS) and Internet of Everything (IoE), Internet of Services (IoS) has recently emerged as a new buzzword in the field of service computing. Providing accurate and personalized service recommendations to users is essential yet highly challenging in IoS, from a sea of services. Besides the severe sparsity of users’ historical behavior data on services, little study has been reported in the literature on fully exploiting multiple relationship networks embedded in IoS. To fill this gap, we propose a novel Multi-Graph and Multi-Aspect neural network-powered method for Service Recommendation in IoS. Graph neural networks (GNNs) and attention mechanism are jointly employed to simultaneously extract information from a collection of heterogeneous knowledge graphs, constructed from historical data recorded in IoS including the user-service interaction graph, the user-user social graph, and the service-mashup graph. Based on the knowledge learned, user-service interactions are scrutinized from multiple aspects to better learn the multiple preferences of users and the multiple characteristics of services, in order to refine their profiles for future recommendation. The results of extensive experiments over the real-world datasets have demonstrated that MGMASR outperforms the baseline methods and can provide service recommendations more accurately for users in IoS.

Next location recommendation services play a pivotal role in Location-Based Social Networks (LBSNs) due to their ability to provide personalized recommendations of attractive destinations, resulting in substantial benefits for both users and service providers. Recent research indicates that these services are influenced by both sequential and geographical factors. However, we argue that most of these services fail to fully exploit the latent multi-group knowledge of location semantics and user preferences, resulting in suboptimal performance. Therefore, we propose STMGCL, a novel spatial-temporal multi-group contrastive learning-based method to discover intrinsic multi-group information for improving next location recommendation services. Specifically, STMGCL designs Spatial Group Contrastive Learning (SGCL) to extract multiple group knowledge regarding location semantics. Additionally, it develops Temporal Group Contrastive Learning (TGCL) to explore multiple user preference group information through a self-attention based encoder. Finally, we leverage a multi-task learning strategy and a generalized Expectation Maximization (EM) algorithm to ensure that STMGCL is optimized end-to-end with guaranteed convergence. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of STMGCL over baselines.

Knowledge services are becoming a rising star in the family of XaaS (Everything as a Service). In recent years, people are more willing to search for answers and share their knowledge directly over the Internet, which makes the knowledge service ecosystem prosperous. In this paper, we aim to predict the popularity of knowledge services, which will benefit the downstream industries. Toward such a task, the spatial interactions (e.g., hyperlinks in Wikipedia) and temporal observations (e.g., page views) provide crucial information. However, it is difficult to utilize this information due to: (i) complicated and different usage observations, (ii) intricate and evolutionary spatial interactions, and (iii) small world trait of the network. To tackle such issues, we propose evolutionary graph convolutional recurrent neural networks (E-GCRNNs) to simultaneously model both temporal and spatial dependencies of knowledge services from their evolving networks. Additionally, a localized mini-batch training scheme is developed, which allows the E-GCRNNs to work on large-scale knowledge services network and reduce the prediction bias caused by the small world trait. Extensive experiments on real-world datasets have demonstrated that the proposed E-GCRNNs outperform baselines in terms of prediction accuracy, especially with the prediction range being longer, while remaining computationally efficient.

Driven by Service-Oriented Computing techniques, time-aware service recommendation aims to support personalized mashup development, adapting to the rapid shifts of users' dynamic preferences. Recent studies have revealed that users' social connections may help better model their dynamic preferences. However, two phenomena exist to influence users' dynamic preferences of service selection. First, users and their friends may only share preferences in certain services, which means not every service in the friends' consumed mashups has the same impact on a target user's dynamic preference. Second, for a target user, friends in his social network with similar interests and behaviors may contribute more influence intensities. To cover the above phenomena synergistically, this paper proposes a Social-powered Graph Hierarchical Attention Network (SGHAN), as a deep learning model capable of learning similar behaviors from proper friends during mashup development. SGHAN is powered by the reciprocity between its two core components: a service-level attentional encoder captures users' interested services in friends' mashups, while a friend-level graph attention network selects informative friends and propagates the friends' social influences. Extensive experiments show that the SGHAN model consistently outperforms the state-of-the-art methods in terms of prediction accuracy for mashup creation.

As an emerging concept, Knowledge as a Service (KaaS) aims to provide on-demand content-based (data, information, knowledge) delivery to meet the needs of users. With the prosperity of knowledge services, the prediction of the usage tendency of knowledge services has become an important and timely research topic. This study focuses on speculating the possible popularity of knowledge services in the next period of time, which can assist other downstream service tasks such as service recommendations. The interactions among knowledge services and their rich information (such as historical usage observation and text information) provide grounding for predicting the usage trend of services. However, recent spatial-temporal prediction based on graph neural networks usually depends heavily on the quality of manually created graphs, which may be expensive for knowledge services. To tackle such a limitation, this article proposes a novel Multi-modal Reciprocal SpatioTemporal (MRST) framework, which can jointly mine spatial dependencies and model time patterns for spatiotemporal coupling prediction. Two types of Edge Inference Networks (called EIN-o and EIN-t) are designed to sufficiently discover the spatial dependencies among knowledge services based on the data of usage observation sequences and service descriptions, respectively, and generate multi-modal directed weighted knowledge service graphs. Based on these graphs, MRST integrates GCN-based spatiotemporal prediction models as backbones to make predictions. Particularly, MRST features a unique reciprocal framework. On the one hand, EINs infer and generate multi-modal graphs to serve GCNs; on the other hand, GCNs utilize such spatial dependencies to make predictions and then introduce feedback to optimize EINs. In the meantime, to facilitate reproducible research, we collect a new knowledge service dataset fromWikipediacalled Wiki-EN dataset. Experiments on this real data set show that the proposed MRST framework significantly surpasses the baselines and can learn meaningful spatial dependencies outside the predefined graphic structure.

Web service recommendation remains a highly demanding yet challenging task in the field of services computing. In recent years, researchers have started to employ side information comprised in a heterogeneous Web service ecosystem to address the issues of data sparsity and cold start in Web service recommendation. Some recent works have exploited the deep learning techniques to learn user/Web service representations accumulating information from multiplex sources. However, we argue that they still struggle to utilize multi-source information in a discriminating, unified and flexible manner. To tackle this problem, this paper presents a novel multi-source information graph-based Web service recommendation framework (MGASR), which can automatically and efficiently extract multifaceted knowledge from the heterogeneous Web service ecosystem. Specifically, different node-type and edge-type dependent parameters are designed to model corresponding types of objects (nodes) and relations (edges) in the Web service ecosystem. We then leverage graph neural networks (GNNs) with an attention mechanism to construct a multi-source information neural network (MIN) layer, for mining diverse significant dependencies among nodes. By stacking multiple MIN layers, each node can be characterized by a highly contextualized representation due to capturing high-order multi-source information. As such, MGASR can generate representations with rich semantic information toward supporting Web service recommendation tasks. Extensive experiments conducted over three real-world Web service datasets demonstrate the superior performance of our proposed MGASR as compared to various baseline methods.

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN’s updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time.Note to Practitioners—Most scientific applications are computation and/or data-intensive and need large-scale or high-performance resources for their execution. More and more scientists use workflows to manage their applications, but how to efficiently run them in the cloud is a big challenge due to their large scale as well as the dynamic characteristics of the elastic and heterogeneous cloud resources. In this article, we develop a novel multiobjective optimization technique for workflow scheduling such that the makespan and cost can be minimized simultaneously. A scoring criterion, dynamic hierarchical structure and its evolutionary rules, and adaptive adjustment strategies are designed to cooperate with each other and increase the search ability and efficiency of the original and widely used NSGA-II. Adequate experiments are conducted to verify the proposed method’s performance, and the experimental results show that it can provide more near-optimal solutions than the existing methods. It can be readily applied for implementing more efficient and effective cloud data centers to execute large-scale scientific workflows.

In modern discrete flexible manufacturing systems, dynamic disturbances frequently occur in real time and each job may contain several special operations in partial-no-wait constraint due to technological requirements. In this regard, a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named hierarchical multi-agent proximal policy optimization (HMAPPO) is developed to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO contains three proximal policy optimization (PPO)-based agents operating in different spatiotemporal scales, namely, objective agent, job agent, and machine agent. The objective agent acts as a higher controller periodically determining the temporary objectives to be optimized. The job agent and machine agent are lower actuators, respectively, choosing a job selection rule and machine assignment rule to achieve the temporary objective at each rescheduling point. Five job selection rules and six machine assignment rules are designed to select an uncompleted job and assign the next operation of which together with its successors in no-wait constraint on the corresponding processing machines. A hierarchical PPO-based training algorithm is developed. Extensive numerical experiments have confirmed the effectiveness and superiority of the proposed HMAPPO compared with other well-known dynamic scheduling methods. Note to Practitioners—The motivation of this article stems from the need to develop real-time scheduling methods for modern discrete flexible manufacturing factories, such as aerospace product manufacturing and steel manufacturing, where dynamic events frequently occur, and each job may contain several operations subjected to the no-wait constraint. Traditional dynamic scheduling methods, such as metaheuristics or dispatching rules, either suffer from poor time efficiency or fail to ensure good solution quality for multiple objectives in the long-term run. Meanwhile, few of the previous studies have considered the partial-no-wait constraint among several operations from the same job, which widely exists in many industries. In this article, we propose a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named HMAPPO to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO uses three DRL-based agents to adaptively select the temporary objectives and choose the most feasible dispatching rules to achieve them at different rescheduling points, through which the rescheduling can be made in real time and a good compromise among different objectives can be obtained in the long-term schedule. Extensive experimental results have demonstrated the effectiveness and superiority of the proposed HMAPPO. For industrial applications, this method can be extended to many other production scheduling problems, such as hybrid flow shops and open shop with different uncertainties and objectives.

Driven by proliferation of the Service-Oriented Architecture (SOA), the quantity of published software services and their users keeps increasing rapidly in the service ecosystem; thus, personalized service selection and recommendation has remained a hot topic. Recent studies have revealed that users’ social connections may help better model their potential behaviors. Therefore, in this paper, we study how users’ high-order social networks may help improve service recommendation as well as its explainability. Two observations are set forth. First, a user’s service preference may be influenced by his trusted users, whom in turn influenced by their social connections. Second, such chained influences will not remain static and equally-weighted, as a user’s confidence over his social relations may vary confronted with different targeted services. We thus introduce a novel High-order Social Graph Neural Network (HSGNN) to support social-aware service recommendation. The key idea of the model is a graph convolution-based, multi-hop propagation module devised to extract the high-order social similarity signals from users’ local social networks, and encode them into the users’ general representations. Afterwards, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users’ specific preference. Extensive experiments in a real-world service dataset show that our HSGNN makes service recommendation more accurately, i.e., by 4.71% in terms of normalized discounted cumulative gain (NDCG), than state-of-the-art baseline methods.

Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while meeting the deadline constraint of each workflow. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improve global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.

Driven by the widespread application of Service-Oriented Architecture (SOA), an increasing number of services and mashups have been developed and published onto the Internet in the past decades. With the number keeping on burgeoning, predicting the tendency of services invocation will provide various roles in service ecosystems with promising opportunities. However, services invocation bear three unique characteristics, which give rise to difficulties in predicting them. First, enormous services show different and complicated traits, like periodicity, nonlinearity and nonstationarity. Second, services providing similar or compensatory functions make up intricate relationship. Third, the combination dependencies between mashups and their comprising component services further amplify the difficulty. Given these factors, we have developed a tailored model Multi-Step Piecewise Recurrent Neural Network (MSP-RNN) to predict the tendency of services invocation. In MSP-RNN, Long Short Term Memory (LSTM) units are used to extract universal features. Based on these features, we have developed a piecewise regressive mechanism to make prediction discriminatingly. Besides, we have developed a multi-step prediction strategy to further enhance prediction accuracy and robustness. Extensive experiments in real-world data set with interpretable analysis show that MSP-RNN predicts the tendency of services invocation more accurately, i.e., by 3.7 percent in terms of symmetric mean absolute percentage error (SMAPE), than state-of-the-art baseline methods.

With the boom of Web services, there is a growing need for visualizing service ecosystems to help people browse services and understand their functionalities and positions in the systems. One foundational step of building a proper visualization is to ensure accurate representations for the comprising services. However, it is not a trivial task as service profiles may not be sufficient for two significant reasons. First, while the services themselves being used in various scenarios, their profiles may not always precisely reflect all of them. Second, service profiles usually comprise quite a few universal background terms that cannot distinguish services. To address these two issues, we apply machine learning techniques to incrementally learn service representations in a whole. A tailored topic model is developed, named Service Representation-Latent Dirichlet Allocation (SR-LDA). The core idea is to learn more comprehensive and up-to-date information about services from the profiles of the involved service compositions (i.e., mashup profiles), while introducing a global filter to identify and filter out background terms. Both quantitative and qualitative experiments on a real-world dataset demonstrate that the proposed SR-LDA builds higher-quality service representations comparing with baselines. We further generate a knowledge map to visualize a service ecosystem based on the learned service representations. Such a knowledge map directly leads to the detection of four typical functionality patterns of Web services and serves the purpose of mashup creation.

Service recommendation for composition creation is a widely applied technique, which expedites mashup development by reusing existing services. The core of service recommendations is to simultaneously understand user needs as well as the functions of available services. However, the descriptions provided by users and service providers may not always be accurate or up to date, which poses significant challenges to composition creating. To tackle this problem, in this paper we propose a deep learning-based service recommendation framework named coACN, short for Collaborative Attention Convolutional Network, which can effectively learn the bilateral information toward service recommendation. On the one hand, a domain-level attention module is constructed to refine user needs embeddings by drawing messages from related service domains. On the other hand, a graph convolutional network is established to excavate the service-composition graph and fuse structured information into service embeddings. For a service node in the graph, the information of its compositions as its first-order neighbor nodes is used to supplement the latest functions and features of the service; and the information of the services as its second-order neighbor nodes may bring collaborative relationships into the service. Extensive experiments on the real-world ProgrammableWeb dataset show the significant improvement of our proposed coACN framework over state-of-the-art methods.

The number of available online services increases sharply with the development of the Internet. These services typically belong to varying service domains. To address the data-sparse issue, cross-domain recommendation techniques are proposed to transfer the information in relevant service domains to improve the recommendation effects. In this paper, we presented a novel end-to-end cross-domain service recommendation learning framework, named EATN, short for End-to-end Attention Transfer Network, which is different from most existing cross-domain step-by-step learning frameworks. To realize this end-to-end framework, we design a workflow to achieve user preferences cross-domain matching procedure. We capture fine-grained and multi-faceted user preferences by using multiple Multi-Layer Perceptron layers. To reasonably integrate multi-faceted transfer preferences, we design a service-level attention module, which learns weight based on the relevance to services. Finally, it can improve the recommendation effect of cold-start users in the target domain. Extensive experiments on the real-world Amazon dataset show the significant improvement of our proposed EATN framework.

In recent years, Graph Convolutional Networks (GCNs) have been applied to benefit spatiotemporal predictions. The current shell for spatiotemporal predictions often relies heavily on the quality of handcraft, fixed graphical structures, however, we argue that such a paradigm could be expensive and sub-optimal in many applications. To raise the bar, this paper proposes to jointly mine the spatial dependencies and model temporal patterns in a coupled framework, i.e., to make spatiotemporal-coupled predictions. We come up with a novel Reciprocal SpatioTemporal (REST) framework, which introduces Edge Inference Networks (EINs) to couple with GCNs. From the temporal side to the spatial side, EINs infer spatial dependencies among time series vertices and generate multi-modal directed weighted graphs to serve GCNs. And from the temporal side to the spatial side, GCNs utilize these spatial dependencies to make predictions and then introduce feedback to optimize EINs. The REST framework is incrementally trained for higher performance of spatiotemporal prediction, powered by the reciprocity between its comprised two components from such an iterative joint learning process. Additionally, to maximize the power of the REST framework, we design a phased heuristic approach, which effectively stabilizes training procedure and prevents early-stop. Extensive experiments on two real-world datasets have demonstrated that the proposed REST framework significantly outperforms baselines, and can learn meaningful spatial dependencies beyond predefined graphical structures.

Existing Web service eco-systems are typically managed in a centralized manner, which hinders their further development due to inherent disadvantages such as trust issues, interest disputes, value separation and so on. The recently emerged blockchains provide distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states, which provide a natural solution. Based on the INKchain, which is an open-source permissioned blockchain mechanism extending the Hyperledger Fabric, this paper proposes Trusted Decentralized Service Eco-System (T-DSES). T-DSES achieves not only fundamental functionalities of conventional systems, but also offers mechanisms to stimulate participants to bring trustworthiness to the whole system. The trustworthiness of T-DSES is realized by three strategies: reliable information of services and mashups, reliable records of participants’ rights, and reliable measurement of participants’ contributions. A customized token “SToken” is created to act as the media of value circulation. In this paper, the overall framework and detailed design of T-DSES are presented, especially including how to utilize Kubernetes to establish a cloud-based environment. A tailored Web front-end ensures the usability of operations. Over real-world data from ProgrammableWeb.com, analyses and experiments have been conducted to verify the feasibility and effectiveness of the presented approach.

With the widespread application of Service-Oriented Architecture, the quantity of web services keeps increasing rapidly over the Internet. Providing personalized service recommendation to users remains to be an important research topic. Recent studies have proved social connections helpful for modeling users' potential preference thus improving the performance of service recommendation. To date, however, one special type of social relation, called high-order social relation, has not been thoroughly studied. In reality, a user's preference may not only be affected by the user's direct neighbors, but also indirect ones. Furthermore, such influences may not remain static in the context of various attentions. To tackle such issues, we have developed a novel neural Attentive network based on High-order Social Graph (A-HSG) toward offering social-aware service recommendation. First, a graph convolution-based, multi-hop propagation module is devised to extract the high-order similarity signals from users' local social networks, and inject them into the users' general representations. Second, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users' specific preference. Extensive experiments over a real-life service dataset show that A-HSG outperforms baseline methods in terms of prediction accuracy.

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.

Service ecosystems, consisting of various kinds of services and mashups, usually keep evolving over time.Existing works on the evolution of service ecosystems focus on either evaluating the impacts of single services' changes on the usage of services and the stability of the whole ecosystem, or discovering co-occurrence relationship between services, but fail to disclose any knowledge from the aspect of the evolution of service composition patterns.Based on our previous work, this paper moves one step further, revealing the latent service composition trends in a service ecosystem and providing more distinct explanation of different topic evolution patterns.A novel methodology, named Extended Dependency-Compensated Service Co-occurrence LDA (EDC-SeCo-LDA), is developed to calculate the directed dependencies between different topics and build topic evolution graph.The evolution trend

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.

Existing service ecosystems typically rely on some centralized service registries (e.g., ProgrammableWeb.com) as "middle people" to record service behaviors thus to provide service ranking and recommendation. Excessive centralization increasingly becomes the bottleneck and hinders the further growth of the service ecosystems. As the first attempt to apply the fundamental technique underneath the emerging Bitcoin network into the field of service oriented computing, this paper proposes to build a service ecosystem as a decentralized blockchain-oriented service network, called Decentralized Service Eco-System (DSES). Whenever any activity occurs in the system (e.g., APIs are used together in a published mashup), all involved parties will individually store and maintain a copy of the detailed record (provenance) at their local databases. Such a distributed database-oriented solution will enable services who do not fully trust each other to maintain a set of global states. In this way, service discovery and recommendation can be realized in a distributed manner that promises higher scalability and maintainability. As a proof of concept, a prototyping system of DSES is constructed using the real-world data from ProgrammableWeb.com, based on the INKchain, a newly open-source consortium blockchain mechanism extending the Hyperledger Fabric.

Driven by the widespread application of Service-Oriented Architecture (SOA), the quantity of web services and their users keeps increasing in the service ecosystem. Since services are hosted by service providers, it will be very helpful to predict the tendency of services invocation for service providers, so that proper actions may be taken to ensure the quality of services. Two major challenges exist in predicting the tendency of services invocation, however. First, different service invocation sequences may bear different and complicated characteristics, which is hard to be modeled generally. Second, the intricate relations between service invocation sequences are valuable but hard to be discriminated and utilized. To address these issues, a deep neural network, named Piecewise Recurrent Neural Network (PRNN), is developed by taking both generality and pertinence into consideration. For generality, PRNN extracts complicated characteristics of all service invocation sequences through Long Short-Term Memory (LSTM) units. For pertinence, PRNN develops a piecewise mechanism, through which service invocation sequences can be clustered automatically and predicted discriminatingly. Extensive experiments in real-world dataset show that PRNN outperforms baseline methods in predicting the tendency of services invocation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Web services usually compose to workflows to satisfy complex demands. End-to-end execution time is widely seen as a key quality metric of web service workflows. That will be affected by many factors. This paper focuses on impacts of an important factor -- scheduling algorithm in services -- on collective end-to-end time characteristics of a set of web service workflows. We develop a novel simulator, in which workflows are simulated to execute. Impacts of different scheduling algorithms are evaluated through comparing simulation results. Simulation results indicate that maximal and average end-toend execution time of most workflows when using "earliest deadline first" (EDF) scheduling algorithm in services is significant shorter than that when using widely used "first in, first out" (FIFO) scheduling algorithm.

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.

With more and more applications of the service-oriented methods in the business area, an overwhelming number of business services have arisen. These business services collaborate with each other and form a business network or a services-oriented business ecosystem (SOBE). The business services selection is a key issue for sharing and integrating of business services in the SOBE. There is a general assumption that only one specific business service will be selected for every business node in a business process in the related researches of services selection. However, in actual business collaboration, it is needed to choose two or more business services for one business node to complete the business requirements including functional and non-functional needs. So the multi-services selection method for one business node based on QoS attributes was put forward in this paper. First, notations used in this paper were defined. Second, a multi-services selection method is proposed by assuming the parallel mode of these services. And the total QoS attributes calculation method of services selected for one business node is given. Third, with the multi-services selection method, a goal programming model is established for the business services selection with QoS attributes by considering that multi-services worked for one business node in the parallel mode. The object of this model is to minimize the total execution time difference and the total execution cost difference in lexicographic order. The constraints are functions of QoS attributes including services' execution time, execution cost, availability, and reliability. Then, the goal programming model is solved using the GA-based algorithm. Finally, an instance based on the Zhejiang business model is set up, and numerical calculation results are used to demonstrate feasibility and effectiveness of the proposed method.

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.

In the process of web service composition, the check and prevention of semantic incompatibility is one of the most important issues. In this paper, a controlled Petri net (CtlPN)-based model for web service composition is proposed. Meanwhile, the optimal controller is constructed, such that the appropriate vectors of controllable place and arc are appended in the key transition which can lead to deadlock states. In addition, for the semantic incompatibility case, a policy based on appending optimal controller is presented. It is proved that our policy can be a good solution. Finally, the proposed controller is transformed as the activity of BPEL.

Current methods that deal with concurrent workflow temporal violations only focus on checking whether there are any temporal violations. They are not able to point out the path where the temporal violation happens and thus cannot provide specific solutions. This paper presents an approach based on a sprouting graph to find out the temporal violation paths in concurrent workflow processes as well as possible solutions to resolve the temporal violations. First, we model concurrent workflow processes with time workflow net and a sprouting graph. Second, we update the sprouting graph at the checking point. Finally, we find out the temporal violation paths and provide solutions. We apply the approach in a real business scenario to illustrate its advantages: 1) It can dynamically check temporal constraints of multiple concurrent workflow processes with resource constraints; 2) it can give the path information in the workflow processes where the temporal violation happens; and 3) it can provide solution to the temporal violation based on the analysis.

Web services have become the technology of choice for service-oriented computing implementation, where Web services can be composed in response to some users' needs. It is critical to verify the compatibility of component Web services to ensure the correctness of the whole composition in which these components participate. Traditionally, two conditions need to be satisfied during the verification of compatibility: reachable termination and proper termination. Unfortunately, it is complex and time consuming to verify those two conditions. To reduce the complexity of this verification, we model Web services using colored Petri nets (PNs) so that a specific property of their structures is looked into, namely, well structuredness. We prove that only reachable termination needs to be satisfied when verifying behavioral compatibility among well-structured Web services. When a composition is declared as valid and in the case where one of its component Web services fails at run time, an alternative one with similar behavior needs to come into play as a substitute. Thus, it is important to develop effective approaches that permit one to analyze the similarity of Web services. Although many existing approaches utilize PNs to analyze behavioral compatibility, few of them explore further appropriate definitions of behavioral similarity and provide a user-friendly tool with automatic verification. In this paper, we introduce a formal definition of context-independent similarity and show that a Web service can be substituted by an alternative peer of similar behavior without intervening other Web services in the composition. Therefore, the cost of verifying service substitutability is largely reduced. We also provide an algorithm for the verification and implement it in a tool. Using the tool, the verification of behavioral similarity of Web services can be performed in an automatic way.

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.

Business process execution language for Web services (BPEL) is becoming the industrial standard for modeling Web-service-based business processes. Behavioral compatibility for Web service composition is one of the most important topics. The commonly used reachability exploration method focuses on verifying deadlock freeness. When this property is violated, the states and traces in the reachability graph only give clues to redesign the composition. The redesign must then repeat itself until no deadlock is found. In this paper, multiple Web service interaction is modeled with a Petri net called composition net (C-net for short). The problem of behavioral compatibility among Web services is hence transformed into the deadlock structure problem of a C-net. If services are incompatible, a policy based on appending additional information channels is proposed. It is proved that the policy can offer a good solution that can be mapped back into the BPEL models automatically.

The potential benefits of Web services composition heavily rely on semantic interoperability, i.e., the ability to exchange data meaningfully amongst Web services. Context heterogeneity, which refers to different implicit assumptions about interpreting the exchanged data, hampers the automatic composition of Web services. However, existing initiatives of semantic Web services (SWSs) often ignore context heterogeneity. In this paper, we introduce an approach to address this issue. The contexts of the involved Web services are defined in a lightweight ontology and their WSDL descriptions are annotated by an extension of a W3C standard, i.e., semantic annotation for WSDL and XML schema (SAWSDL). The composition of Web services is described using BPEL specification. Given a BPEL file that ignores context heterogeneity, the approach automatically detects all context differences among the involved services, and reconciles them by producing a mediated BPEL file that incorporates necessary conversions using Xpath functions and/or Web services.

Instances dwelling times (IDT) which consist of waiting times and handle times in a workflow model is a key performance analysis goal. In a workflow model the instances which act as customers and the resources which act as servers form a queuing network. Multidimensional workflow net (MWF-net) includes multiple timing workflow nets (TWF-nets) and the organization and resource information. This paper uses queuing theory and MWF-net to discuss mean value and probability distribution density function (PDDF) of IDT. An example is used to show that the proposed method can be effectively utilized in practice.

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.

With the popularity of Internet technology, Web services are becoming the most promising paradigm for distributed computing. However, when a single Web service fails to meet service requestor's multiple function needs, web services need to be dynamically configured together to form a web service composition. Since there may be many configurations providing identical functionality with different quality-of-service (QoS), a choice needs to be made according to users' functional and nonfunctional requirements. In this paper, we formulate a Web service functional configuration problem by using Petri nets. The graph structure and algebraic properties of the model are analyzed in detail to show that a basis solution of a state-shift equation of the Petri net model corresponds to a realizable configuration process. This result is later used to formulate the multiple attribute QoS optimization problem to a linear programming problem. Finally, a case study is performed to show that the proposed analysis result can be effectively applied in practice.

Enterprise cooperation is a critical issue to improve the company’s competition capability nowadays. The business cooperation modeling has been received a lot of attention due to its importance. The effort can be classified into two types: cooperation process description and implementation-oriented modeling. The business description is usually easier for users to understand but is hard to realize. The paper discusses business cooperation scenarios from perspectives of partners, types, control and data and analyzes its modeling requirements. Then a multi-view cooperation model is proposed to describe cooperation process. It has graphic process view which can be easily understand. A model mapping technique is put forward to transfer the cooperation description model to BPEL model that can be executed by service-oriented workflow engine. The prototype of service-oriented business cooperation system is developed.

Service composition is one of the key objectives for adopting service oriented architecture. Today, Web services, however, are not always perfectly compatible and composition mismatches are common problems. Service mediation, generally classified into signature and protocol ones, thus becomes one key working area in SOA. While the former has received considerable attention, protocol mediation is still open and current approaches provide only partial solutions. In this paper, a pattern-based approach is proposed for developers to semi-automatically generate mediators and glue partially compatible services together. Based on the investigation on workflow patterns and message exchanging sequences in service interactions, several basic mediator patterns are developed and can be used to modularly construct advanced mediators that can resolve all possible protocol mismatches, especially such mismatches about complicated control logics. Moreover, the architecture for the service mediation system is designed and implemented to prove the feasibility of our approach.

Abstract In service‐oriented architecture, services and business processes are closely related and therefore the research on service‐oriented business process (SOBP) attracts the attention of academia as well as industry. Because of the loosely coupled, autonomic and dynamic nature of services, the operation and the performance evaluation of business process meet some challenges, such as the definition of the key performance indicators (KPIs) and the alignment of business performance and IT performance. In this paper, we address these challenges. First, the definition and the characteristics of an SOBP are presented with a motivating scenario. Then a service‐oriented business performance evaluation model is described, which integrates the performance of strategy layer, business process layer, business activity layer, service composition layer and IT infrastructure layer. The KPIs corresponding to each layer are also defined with their formal representations. The qualitative and quantitative performance analysis methods, based on the proposed model, are presented, respectively. Finally, the improved analytic hierarchy process is explicated to calculate the correlation between different KPIs and select the most suitable service. An online‐shopping example is taken to prove the soundness and the feasibility of the method. Copyright © 2008 John Wiley & Sons, Ltd.

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.

With the development of enterprise-wide and cross-enterprise application integration and interoperation toward Web service, Web service providers try to not only fulfill the functional requirements of Web service users but also satisfy their nonfunctional conditions in order to survive in the competitive market. A hot research topic is how to configure Web services to meet their demand when the diversity of user requirements, distinction of service components' performance, and limitation of resources are considered. This paper builds a Web service configuration net based on Petri nets in order to exhibit Web service configurations in a formal way. Then, an optimal algorithm is presented to help choose the best configuration with the highest quality of service to meet users' nonfunctional requirements. Finally, the simulation results and related analysis prove the soundness and correctness of our model and algorithm.

The advent of SOA and Grid technology has brought new challenges to workflow operation and performance evaluation. In this paper, the characteristics of service-oriented workflow are presented, based on which the service-oriented workflow performance evaluation model is described and the performance analysis methods are depicted. Finally the design and implementation of our prototype system are introduced briefly.

In Service Oriented Architecture (SOA), service brokers could find many service providers which offer same function with different quality of service (QoS). Under this condition, users may encounter difficulty to decide how to choose from the candidates to obtain optimal service quality. This paper tackles the Service Selection Problem (SSP) of time-sensitive services using the theory of games creatively. Pure strategies proposed by current studies are proved to be improper to this problem because the decision conflicts among the users result in poor performance. A novel Cooperative Mixed Strategy (CMS) with good computability is developed in this paper to solve such inconstant-sum non-cooperative n-person dynamic game. Unlike related researches, CMS offers users an optimized probability mass function instead of a deterministic decision to select a proper provider from the candidates. Therefore it is able to eliminate the fluctuation of queue length, and raise the overall performance of SOA significantly. Furthermore, the stability and equilibrium of CMS are proved by simulations.

With the development of enterprise-wide and cross-enterprise application integration and interoperation towards web service, web service providers try to not only fulfill the functional requirements of web service users, but also satisfy their non-functional conditions in order to survive in the competitive market. A hot research topic is how to configure web services to meet their demand under a dynamic heterogeneous environment. This paper builds a web service configuration net based on Petri nets in order to exhibit web service configuration in a formal way. Then, an optimal algorithm is presented to help choose the best configuration with the highest quality of services (QoS) to meet users’ non-functional requirements. Finally, the simulation results and related analysis prove the soundness and correctness of our model and algorithm.

Aiming at achieving a flexible, reconfigurable and integrated product design and development process, a generic product modeling framework is introduced to give full consideration of mass customization and product lifecycle management. Modules, platform, general product structure construct the overall logical organization of product families covering four domains across the lifecycle. Based on it, modes of product design and innovations are further discussed

Workflow model analysis is performed at logic, temporal, and performance levels. This paper mainly deals with the performance level issues. Workflow net (WF-net) is extended with time information to the timing workflow net (TWF-net). To provide a formal framework for modeling and analyzing workflow, this paper proposes a multidimension workflow net (MWF-net) that include multiple TWF-nets and the organization and resource information. The algorithm to decompose a free-choice and acyclic Petri nets (PN) into a set of T-components is extended to a TWF-net containing iteration structures. Then, resource availability and workload analysis is performed. A method for computing the lower bound of average turnaround time of transaction instances processed in a MWF-net is proposed. Finally a case study is used to show that the proposed method can be effectively utilized in practice.

Multi-dimension Workflow net (MWF-net), which includes process, organization, and resource perspectives, is introduced. Using the structure analysis of the TWF-net in the process perspective and the perspectives mapping, the routing of transaction instances in the multi-TWF-nets can be projected into the flow of transaction instance between different resource pools in the resource perspective. After the relevant work in is briefly reviewed, the boundedness verification method of a MWF-net is proposed. A MWF-net is bounded implies the corresponding queuing network in the resource perspective has stable solution. Based on the discussion of several operational principles in the context of workflow model, an approximate method for performance analysis of a workflow model is presented.

Uniform ultimate boundedness of a new class of switched linear systems is considered. A switched linear system in this class consists of m subsystems, and none of the individual subsystems need to be stabilizable. The switched linear system is shown to be uniformly ultimately bounded for any pre-given bound if the combination of controllable components of states for all subsystems covers the entire state space. An algorithm for the design of continuous controllers and the switching strategy is given to provide the design of continuous controllers and the switching strategy. Finally, the simulations show the validity of the result.

The analysis of the correctness and rationality of a workflow model plays an important role in the research of workflow techniques and successful implementation of workflow management. This paper points out the relevant problems in the verification and analysis of a workflow model. It discusses two important properties: schedulability and boundedness of a workflow model considering timing constraints. To specify the timing constraints, WorkFlow net is extended with time information, leading to timing constraint workflow net (TCWF-net). This paper presents a model mapping method to convert a directed network graph (DNG) based workflow model, which is built by a graphic process modeling language extended with time information, into a TCWF-net. It then discusses its schedulability verification and synthesis. An algorithm to decompose an acyclic and free-choice TCWF-net into a set of T-components is presented, followed by a boundedness verification method. The usefulness of the research results is illustrated by an example.