The rapid advancement of Large Language Model (LLM)-driven multi-agent systems has significantly streamlined software developing tasks, enabling users with little technical expertise to develop executable applications. While these systems democratize software creation through natural language requirements, they introduce significant security risks that remain largely unexplored. We identify two risky scenarios: Malicious User with Benign Agents (MU-BA) and Benign User with Malicious Agents (BU-MA). We introduce the Implicit Malicious Behavior Injection Attack (IMBIA), demonstrating how multi-agent systems can be manipulated to generate software with concealed malicious capabilities beneath seemingly benign applications, and propose Adv-IMBIA as a defense mechanism. Evaluations across ChatDev, MetaGPT, and AgentVerse frameworks reveal varying vulnerability patterns, with IMBIA achieving attack success rates of 93%, 45%, and 71% in MU-BA scenarios, and 71%, 84%, and 45% in BU-MA scenarios. Our defense mechanism reduced attack success rates significantly, particularly in the MU-BA scenario. Further analysis reveals that compromised agents in the coding and testing phases pose significantly greater security risks, while also identifying critical agents that require protection against malicious user exploitation. Our findings highlight the urgent need for robust security measures in multi-agent software development systems and provide practical guidelines for implementing targeted, resource-efficient defensive strategies.

The Open Digital Rights Language (ODRL) is a pivotal standard for automating data rights management. However, the inherent logical complexity of authorization policies, combined with the scarcity of high-quality ``Natural Language-to-ODRL" training datasets, impedes the ability of current methods to efficiently and accurately translate complex rules from natural language into the ODRL format. To address this challenge, this research leverages the potent comprehension and generation capabilities of Large Language Models (LLMs) to achieve both automation and high fidelity in this translation process. We introduce AgentODRL, a multi-agent system based on an Orchestrator-Workers architecture. The architecture consists of specialized Workers, including a Generator for ODRL policy creation, a Decomposer for breaking down complex use cases, and a Rewriter for simplifying nested logical relationships. The Orchestrator agent dynamically coordinates these Workers, assembling an optimal pathway based on the complexity of the input use case. Specifically, we enhance the ODRL Generator by incorporating a validator-based syntax strategy and a semantic reflection mechanism powered by a LoRA-finetuned model, significantly elevating the quality of the generated policies. Extensive experiments were conducted on a newly constructed dataset comprising 770 use cases of varying complexity, all situated within the context of data spaces. The results, evaluated using ODRL syntax and semantic scores, demonstrate that our proposed Orchestrator-Workers system, enhanced with these strategies, achieves superior performance on the ODRL generation task.

In data-intensive microservice-based systems, frequent and large-scale inter-service communication poses a critical performance bottleneck, degrading throughput and escalating latency. Existing solutions exhibit notable limitations: microservice merging sacrifices loose coupling and system evolvability; resource-aware scheduling enhances communication efficiency but fails to reduce data volume; dynamic deployment reduces network distances while introducing compute-resource contention; and unnecessary data transfer elimination remains ineffective under massive data loads. Hence, to overcome these challenges, we propose iRUC, an approach forinter-service data communicationReduction viaUnifiedComputation. In particular, we first designGraphQL+, an executable declarative language that extends GraphQL with service-interaction semantics to achieve unified, cross-language modeling of data processing and transmission across microservices. Second, we develop anLLM-based multi-agent systemleveraging Claude 4.5 Sonnet and Gemini 2.5 Pro to automatically parse microservice code and synthesize corresponding GraphQL+ models. Third, we implement the unifiedexecution enginefor GraphQL+ models, including the database gateway that preserves microservice database autonomy while enabling cross-database queries. This design enables iRUC to perform the unified modeling and execution of data processing and transmission across microservices, thereby significantly reducing inter-service data transfer while maintaining microservice independence. Experimental evaluation on nine GitHub open-source microservice projects deployed on Huawei Cloud demonstrates iRUC’s effectiveness: compared with the unnecessary transfer elimination, dynamic deployment, and serverless computing approaches, iRUC improves throughput by 5.57×, 1.52×, and 1.87×, respectively, while reducing latency to 7.7%, 40.7%, and 37.4% of those approaches. These results show that iRUC achieves significant performance improvements in large-scale data processing scenarios.

The growing system complexity of microservice architectures and the bilateral enhancement of artificial intelligence (AI) for both attackers and defenders present increasing security challenges for cloud-native operations. In particular, cloud-native operators require a holistic view of the dynamic security posture for the microservice-based cloud-native environment from a defense aspect. Additionally, both attackers and defenders can adopt advanced AI technologies. This makes the dynamic interaction and benchmark among different intelligent offense and defense strategies more crucial. Hence, following the multi-agent deep reinforcement learning (RL) paradigm, this research develops an agent-based intelligent security service framework (ISSF) for cloud-native operations. It includes a dynamic attack graph model to represent the cloud-native environment and an action model to represent offense and defense actions. Then we develop an approach to enable the training, publishing, and evaluating of intelligent security services using diverse deep RL algorithms and training strategies, facilitating their systematic development and benchmarking. The experiments demonstrate that our framework can sufficiently model the security posture of a cloud-native system for defenders, effectively develop and quantitatively benchmark different intelligent security services for both attackers and defenders, and guide further optimization.

Microservice architecture is favored for its loose coupling, reusability, and scalability. However, in data-intensive systems where data is the primary and permanent assets, the dynamic inter-microservice communication leads to a large amount of inter-microservice data transfer. This leads to a significant reduction in throughput and an increase in latency. While existing strategies, including microservice decomposition, deployment and resource optimization, show promise, they overlook the unnecessary inter-microservice data communication in practice, which includes the excessive data exposure and carryover data. This motivates us to develop iRUC, an integrated approach to remove the unnecessary inter-microservice data communications, through integrating the Excessive Data Transmission Removal, Carryover Microservice Upgrade, and Query Language (QL) Statement Composition. By implementing a microservice based data intensive system and deploying it on the public cloud environment, the experimental results confirm the effectiveness of our approach, achieving on average 4.2 × to 5.3 × throughput improvement and 78.6% to 85.9% latency reduction.

Workers in crowdsourcing are evolving from one-off, independent micro-workers to on-demand collaborators with a long-term orientation. They were expected to collaborate as transient teams to solve more complex, non-trivial tasks. However, collaboration as a team may not be as prevalent as possible, given the lack of support for synchronous collaboration and the "competition, collaboration but transient" nature of crowdsourcing. Aiming at unfolding how individuals collaborate as a transient team and how such teamwork can affect an individual's long-term success, this study investigates the individuals' collaborations on Kaggle, a crowdsourcing contest platform for data analysis. The analysis reveals a growing trend of collaborating as a transient team, which is influenced by contest designs like complexity and reward. However, compared with working independently, the surplus of teamwork in a contest varies over time. Furthermore, the teamwork experience is beneficial for individuals in the short term and long term. Our study distinguishes the team-related intellectual capital and solo-related intellectual capital, and finds a path dependency effect for the individual to work solely or collectively. These findings allow us to contribute insights into the collaborative strategies for crowd workers, contest designers, and platform operators like Kaggle.

Transformer has achieved admirable success in sequential tasks. However, the model only considers the order of items in the sequence, not the relative distances, which weakens the relevance between items. To this end, we propose a novel Sequence and Distance Aware Transformer (SDAT) for recommendation systems. Specifically, we first apply the Transformer to handle the interaction between the items effectively. Then, Gated Recurrent Unit can be designed to aggregate information on an item-by-item basis in sequential information, meanwhile, we adopt the attention mechanism to focus on items with smaller time intervals to indicate high relevance. We also add a time gain function to augment the influence weight of recent items. Finally, the processing result of the time information of our integrated items replaces the positional encoding representation of the original Transformer. Extensive experiments on three real-world datasets show that SDAT outperforms state-of-the-art methods.

Summary Web services (or Web APIs) on the Internet tends to encounter various unexpected runtime failures because of their dynamicity and distribution. Self‐adaptation technologies for the service‐based business process can effectively repair runtime failures and improve its success rate. However, the same failures may occur on subsequent invocations because relevant processes do not evolve after failures. This makes the response time of the business processes too long. We proposed a self‐adaptation and distributed knowledge‐based evolution model (SDKEM) to guarantee business processes' stabilities, that is, low failure rates and stable response time. SDKEM adopts a service knowledge base (SKB) to organize services from a provider and uses bridge rules to eliminate semantic conflicts among multiple distributed SKBs. It can automatically trigger the evolution of a service ecosystem through the designed self‐adaptation mechanism. We adopt the “survival of the fittest” principle for crucial elements in the ecosystem during evolution so that ultimately, service‐based processes and services with high stability remain. Experiments show that, with the developed evolution mechanism, runtime failures of business processes significantly reduce. In most cases, their response time and success rates are comparable to those under the running situation where no runtime failure occurs, meaning the runtime failures within a service‐based process are automatically repaired.

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.

The recent emergence of gig economy facilitates the exchange of skilled labor by allowing workers to showcase and sell their skills to a global market. Despite the recent effort on thoroughly examining who workers in the gig economy are and what their experience in the gig economy are like, our knowledge on how exactly do workers provide their skills in gig economy, and how worker's strategies on skill provision and expansion relate to their success in gig economy is still lacking. In this paper, we conduct a case study on a prominent gig economy platform, Fiverr.com, to better understand the provision of skills on it through large-scale, data-driven analysis. In particular, we propose the concept of "skill space" from a network perspective to characterize the relationship between different skills by measuring how frequently workers provide different skills together. Through our analysis, we reveal interesting patterns in worker's provision of skills on Fiverr. We then show how these patterns change over time and differ across subgroups of workers with different characteristics. In addition, we find that providing a set of skills that are highly related with each other correlates with a better overall performance in gig economy, and when workers expand their skillsets, expanding to a new skill that is highly-related to the existing skills takes less time and is associated with better performance on the new skill. We conclude by discussing the implications of our findings for gig economy workers and platform in general.

With the sweeping progress of service computing technology and crowdsourcing, individuals are offering their capability as human services online. Companies are orchestrating human services for complex problem-solving, resulting in the rapid growth of human service ecosystems nowadays. Considering the unique characteristics of human services, like capability growth and human-involving collaboration, it is essential to understand the patterns of the development and collaboration among human services. Therefore, this paper proposes a three-layer time-aware heterogeneous network model to quantify the evolution in the human service ecosystem. Based on the model, an exploratory empirical study is presented to uncover how human service providers and consumers develop their capability in service provision and orchestration, as well as how human services collaborate with each other over time. Insights from the emerging patterns open a gateway for further research to facilitate human service adoption, including human service composition recommendation, human skill expansion suggestion, and systematic mechanism design.

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.

Service clustering is the foundation of service discovery, recommendation and composition. Most of the existing methods mainly use service attribute information and ignore the semantic-based invocation relationships among service users. In fact, mutual invocation relationships between services occur on operations of the corresponding services, while service attributes are the whole service description. Our main challenge may be to effectively combine these two kinds of data for service clustering. To address this issue, we propose a new probabilistic generative model which contains two closely connected parts, one characterizing operation community memberships by using operation invocation relationships, and the other characterizing service cluster memberships by utilizing service attributes. The correlations between these two parts are characterized by the relationships between operation communities and service clusters. To train this model, we provide a nested expectation-maximization algorithm. Experimental results show its superior performance over the existing methods for service clustering.

Semantic Annotation plays an essential role in automatic service discovery and composition. However, existing approaches and tools cannot achieve high annotation quality to ensure the semantic service application. Meanwhile, the semi-automatic strategies for improving the annotation quality are time-consuming. To further improve the efficiency as well as the quality of the annotation, this paper presents an effective method involving human-computer interaction to further optimize the annotation procedure. Besides employing the feedback and propagation strategy to semi-automatically improve the annotation quality, the strategy to involve the manual annotation is developed when the efficiency of semi-automatically strategy is related low. To optimize the manual annotation procedure, a clustering based approach is presented to select the most impacted candidates to optimize the annotation improvement. In addition, to help the annotators to choose the correct annotation, the local ontology restriction based method is further designed to improve the recommendation performance. The experiments show that our approach effectively involving the human intervention can significantly improve the annotation quality, faster the quality improvement procedure and reduce the manual load by increasing the recommendation accuracy.

With the popularity of cloud computing and micro service architectures, various service ecosystems including services, venders, and service-based processes continuously emerge on Internet or in an enterprise. Semantics of services from different venders may be described by distributed domain ontologies. Distributed knowledge brings difficulty to competition and cooperation among services, and hampers the evolution of a service ecosystem. In this paper, we propose a distributed knowledge based evolution model (DKEM) to promote competition and cooperation among services from different venders. DKEM considers stability as key factor in competition, and a stability evaluation model is designed to compute stability of services, venders, and service-based processes according to service invocation histories. Based on the evaluation model, two evolution patterns are given, and they can automatically explore new and more stable cooperation among services by means of runtime self-adaption mechanism. A prototype system for DKEM is implemented and a series of experiments show that DKEM is effective for competition and cooperation among services with distributed knowledge, and, evolved processes have higher stability and response efficiency.

The integration of cloud and Internet of Things (IoT), named CloudIoT, has been considered as an enabler for many different applications. However, the suspicion about the security issue is one main concern that some organizations hesitate to adopt such technologies while some just ignore the security issue while integrating the CloudIoT into their business. Therefore, given the numerous choices of cloud-resource providers and IoT devices, how to evaluate their security level becomes an important issue to promote the adoption of CloudIoT as well as reduce the business security risks. To solve this problem, considering the importance of the business data in CloudIoT, we develop an end-to-end security assessment framework based on software defined network (SDN) to evaluate the security level for the given CloudIoT offering. Specially, in order to simplify the network controls and focus on the analysis about the data flow through CloudIoT, we develop a three-layer framework by integrating SDN and CloudIoT, which consists of 23 different indicators to describe its security features. Then, the interviews from industry and academic are carried out to understand the importance of these features for the overall security. Furthermore, given the relevant evidences from the CloudIoT offering, the Google Brillo and Microsoft Azure IoT Suite, our framework can effectively evaluate the security level which can help the consumers for their CloudIoT selection.

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.

With the advent of Web services, service interoperability has always been an active research issue. In recent years, many approaches have been proposed. However, how to achieve fast composition and guarantee correct and executable composite service remains an open issue. For this problem, this paper presents a three-phase framework for accurate and efficient service interoperability. Since service collaborations should follow certain constraints for success invocation, the goal of the first phase is to automatically clarify constraints on Web services. And then the second phase utilizes constraints acquired in previous phase to check Web services' constraint compatibility for accurate collaborations. In order to reduce the time on exhaustive analysis of service matching, the concept of expanded parameters is proposed, thus the problem of semantic matching is transformed into set operation. Subsequently, the third phase achieves interoperability among Web services by constructing initial composite services on the basis of collaborations, optimizing initial compositions to generate minimal composition alternatives with no redundant Web services, and executing final composition services. Experimental results show that our framework can dramatically reduce the time spending on service matching and effectively generate minimal composition alternatives in a rather short time.

Android system has been the crucial platform for the mobile service ecosystem. As a typical open source project, the release of the android system is a challenging issue because many developers are working on the related projects and it will affect millions of mobile service running on the platform. Therefore, investigating the release process of Android system is important for the mobile service ecosystem. Particularly, in this paper, we will focus on the release features prediction issue of what features should be included in the new publishing version. The valid changes and release notes are transformed into low-dimensional vectors and then the automatic labelling methodology is developed to detect the features. Combing with the time series forecasting model, an approach to predict the published features in the new version is presenting. Based on the data collected from the Android Open Source Project (AOSP), the experiments show that: comparing with the state-of-the-art, our approach achieves 13.83% to 17.69% precision improvement in releasing feature predictions and we can effectively detect the spike features for further compatibility management.

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.

Community detection has been one of the key research topics in the analysis of networked data, which is a powerful tool for understanding organizational structures of complex networks. One major challenge in community detection is to analyze community structures for streaming networks in real-time in which changes arrive sequentially and frequently. The existing incremental algorithms are often designed for edge-grained sequential changes, which are sensitive to the processing sequence of edges. However, there exist many real-world net-works that changes occur on node-grained, i.e., node with its connecting edges is added into network simultaneously and all edges arrive at the same time. In this paper, we propose a novel incremental community detection method based on modularity optimization for node-grained streaming networks. This method takes one vertex and its connecting edges as a processing unit, and equally treats edges involved by same node. Our algorithm is evaluated on a set of real-world networks, and is compared with several representative incremental and non-incremental algorithms. The experimental results show that our method is highly effective for discovering communities in an incremental way. In addition, our algorithm even got better results than Louvain method (the famous modularity optimization algorithm using global information) in some test networks, e.g., citation networks, which are more likely to be node-grained. This may further indicate the significance of the node-grained incremental algorithms.

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.

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.

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.

With the upcoming data deluge of semantic data, the fast growth of ontology bases has brought significant challenges in performing efficient and scalable reasoning. Traditional centralized reasoning methods are not sufficient to process large ontologies. Distributed reasoning methods are thus required to improve the scalability and performance of inferences. This paper proposes an incremental and distributed inference method for large-scale ontologies by using MapReduce, which realizes high-performance reasoning and runtime searching, especially for incremental knowledge base. By constructing transfer inference forest and effective assertional triples, the storage is largely reduced and the reasoning process is simplified and accelerated. Finally, a prototype system is implemented on a Hadoop framework and the experimental results validate the usability and effectiveness of the proposed approach.

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 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.