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The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to create linked-microservices (LMS). Linked-microservices are services that run on multiple nodes but closely linked to their linked-partners. Linked-microservices allow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10% - 70%. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service., Comment: IEEE TRANSACTIONS SUSTAINABLE COMPUTING, 2019

The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.

The microservice architecture is an architecture in which a single system is divided into small independently deployed services that are orchestrated together with the use of a lightweight mechanism. Each microservice does not rely much on other microservices (low coupling), but rather on its own resources to perform its task (high cohesion). This paper proposes a novel methodology which decomposes a monolith or other system into microservices in such a way that each microservice will function independently of other microservices while preserving some other key features, with the functionality distribution across each microservice being optimized with regards to usage of the functionality. This methodology makes use of dynamic analysis to identify resources that play a role in enabling the microservice to fulfill its functionality. We establish a set of rules which allows optimized distribution of the functionality. We evaluate the approach by applying it to real systems.

In this paper, four techniques of machine learning (ML) were applied and analyzed during the diagnosis of failures in vehicle fleet tracking modules. A comparison of the sampling methods was carried out considering the training and testing process using real data provided by DDMX, that acts in vehicle fleet tracking. A methodology was defined for pre-processing the collected data before the application of the ML techniques. Totally 16 models were created using the Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) techniques. We have obtained promising results, where the techniques achieved a precision of 99.76% and 99.68% for detection and isolation of faults, respectively, on the provided dataset. These models can serve as prototypes to diagnose faults remotely and states that traditional ML techniques with manual feature extraction are still able to achieve high metrics.

The Internet of Things aims to digitize everyday physical objects by connecting them to the internet. As a result, cyber-physical environments of multiple sizes emerge, imposing new requirements on applications and software systems in regards support to heterogeneity and volatility. A challenging stage in the engineering of these systems is the validation. Although, there have been significant efforts to offer shared real-world testbeds, the simulations platforms are required to make the validation process cost and time effective. Existing simulation approaches only offer partial coverage to the key IoT environment characteristics, focus on communication or are specific for particular use cases and domains. In this paper, we propose a novel agent-based model that enables the simulation of the IoT systems with the key characteristics of an IoT environment. This model is designed to be flexible and adaptable to different experiments. Our approach introduces events in IoT environments as stochastic processes, enabling the evaluation of IoT systems under different conditions that otherwise would be time consuming and costly. We present the results of our experiments for evaluation of our model. These show that our proposal is a practical solution for the validation of IoT software systems, complementary to the real-world tests.

Scheduling is an important topic to support data security for workflow execution in clouds. Some workflow scheduling algorithms use security services such as authentication, integrity verification, and encryption for all workflow tasks. However, applying security services to no sensitive data does not make sense as no benefit is gained, yet it increases the makespan and monetary costs. In this paper, we introduce five policies for selection of tasks that handle sensitive data. We also propose a workflow scheduling algorithm based on a multi-populational genetic algorithm for minimizing cost while meeting a deadline. Experiments using four workflow applications show that our proposal can minimize both the makespan and cost, while maintaining the security of sensitive data compared to another approach in the literature.

In this study, a concept is presented that extends the My Remote previous project to a smart home solution, or the My Smart Remote. The concept extends its management capabilities for children's lives in the home, while at the same time safely and securely sharing collected data about children's activities with external parties including educationalists, health care professionals, psychologists and marketers of products and services used by children. Sharing data not only benefits these external parties but also the children themselves as solutions can be tailored to their needs. Because the My Smart Remote is for children, security and privacy concerns are paramount. A contribution of this concept that is primarily derived from the need for safety and security, and also efficiency in processing, is a solution where much of the sensor data of the My Smart Remote is processed locally, in addition to the cloud. This solution is based on the idea that smart solutions, such as smart cities and smart homes, generate a large amount of sensor data which can be more efficiently processed locally rather than depending on the cloud. The My Smart Remote using sensor data from around the home to manage television viewing, video gaming, activity on PCs, mobile phones and tablets, all in terms of time spent and types of activity. Additionally, the My Smart Remote will monitor these activities which will also include other activities such as taking drinks from the fridge and homework activities and collect, and share the associated data with external parties.

The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.

Every day more and more objects are connected to the Internet to sense or actuate in some environment, composing the Internet of Things. IoT platforms will play a key role, as they will be responsible for managing low-level devices and data acquisition processes, and also support the development of new applications. One of the main challenges in IoT platforms will be the search and discovery of resources in large-scale and heterogeneous environments for reuse by other applications to support their specific requirements. In this paper, we propose an elimination-selection algorithm for search and discovery of resources in IoT environments. Our case study considers a real agricultural problem to be solved by the ViSIoT tool. The results show that our approach improves the quality of the proposed solution adding a small time overhead when compared to the TOPSIS algorithm used by ViSIoT.

Scientific Gateways are one of the most important tools for designing and running experiments. Despite the possibility of local operation they are mainly available via online interfaces based on cloud computing instances. Our studies show that cloud machines may not be the best solution to every situation and that the advantages of heterogeneous cluster machines should be considered in scheduling experiments, saving both financial and computational resources, avoiding network delays and managing the infrastructure as needed. We run a variety of scenarios of bioinformatic experiments in three different sets of machines, a workstation, a cluster and cloud platform. Then, using Support Vector Machines (SVM), a nonlinear regression technique over the results, we can define the best machine configuration in terms of processing time according to the input parameters. The results show an approximation with a small error, that can define with good confidence the proper infrastructure to host a instance of the framework Galaxy, used as study case. With a system based on diverse environments, the researchers can properly schedule each set of experiments.

There are many computational options nowadays, capable to serve properly a wide range of users. Web Services, Cloud Computing, Internet of Things among others are some of the ways technology can be offered. To scientific research this variety is fundamental. One of the major concerns in distributed environments such as cloud computing is the size of data and how to handle it's transport. In some cases, it is necessary to save information in hard drives and send it to other locations. The experiments in Protein Structure Predictions (PSP) usually results in very large amounts of data that are difficult to move from one place to another. Besides that, those results have different computational costs, processing time and price range. The current state-of-art shows the limitations the researchers have to face when working with local computers and most of their work is focused on improve the PSP algorithms. We understand that the historic runs in such systems are a reliable source to be studied and we propose a application of machine learning techniques that can be used as a base for a decision maker capable of defining machine configurations and a proper set-up, while keeping Quality of Service (QoS). The results of the kNN algorithm show a strategy capable of predicting with good accuracy the outcomes such as processing time and resulting file sizes. This defines the base for the decision maker mechanism.

IoT promises a world where Smart Objects (SO) are able to autonomously communicate and work together to make the (human) user's life easier. Popular approaches for development of IoT applications take for granted that on-object resources are evenly constrained. As consequence, we observe a trend towards a data-feeder architecture in which "smart objects" are simple data gatherers and senders. Raw data is stored and processed in cloud platforms feeding web applications and services. The autonomy of SOs is then compromised as they are not able to operate without these platforms. We propose a framework and architecture for the development of IoT applications where smart objects exhibit autonomy in regards to platforms and human users. We completed the successful evaluation of our proposal with the implementation of a prototype and the execution of a use case for physical resources provisioning.

It is a challenging task to ensure quality in service-oriented systems deployed in cloud computing owing to the dynamicity of its environment. Many approaches have been adopted to identify and evaluate bottlenecks and problems in performance. The most common scenario consists of distributed systems that use a workload capable of enabling clients to exploit the target system in different operational conditions. However, one requirement that tends to be overlooked is to determine how the workload is executed, as software and hardware faults can lead to its mischaracterization. In this paper, a number of problems in the workload generation have been identified and summarized. A new architecture, called PEESOS-Cloud, is proposed which allows these services to be evaluated as well as to improve the ability of the workload so that it conforms with its described characteristics. Experiments in a cloud environment were conducted to show how PEESOS-Cloud works and validate its capabilities. Our experiment also showed that the mischaracterization of the workload leads to poor results, whereas an workload-aware implementation leads to a better performance evaluation.

The vast cloud computing environment holds out good prospects for researchers in the computing technology field. However, with several Cloud providers offering different pricing models, the evaluation and modeling of Cloud environments and applications are getting harder because there is a lack of tools for this task. We propose the CM Cloud Simulator to fill this gap since it provides a comprehensive and dynamic simulation of applications with various deployment configurations and incurs the cost it would require when implemented in a Cloud Provider, according to the cost model of any service provider. The CM Cloud Simulator also provides custom-built cost models through the XML file.

Traditional web services transaction processing mechanism handle exception by forward recovery and backward recovery. These compensation mechanisms often lead to waste of resources and time. In this paper, we propose a framework for predicting outcomes of service executions as part of service compositions which allows to choose service instances that are likely to lead to a successful result in the first instance and thus reduces the need for invoking costly recovery mechanisms. The framework makes use of watchdogs to maintain an awareness of service availability and a pre-coordinator which has oversight of the whole composite Web service and acts as a control center. An analysis of a scenario shows that we cannot only provide users with a more satisfactory result, but also can reduce the overhead costs of resources and waste.

Development of IoT applications relies heavily on middleware architectures. Multiple solutions have been proposed using service and agent paradigms, which consequently impose a specific application development model. We observe a trend towards a data-feeder approach in which smart objects are simple data gatherers and senders. This approach reduces the autonomy of smart objects and brings privacy concerns about the data and service manipulation by remote applications and services. We propose a new approach for middleware and application development for smart objects based on web agents and services. Specifically, we integrate services as part of activities included in roles of a web agent allowing us to keep control of the service and data generated on-object, in contrast to delegating to remote applications or services. In addition, our P2P-inspired method for service discovery gives autonomy to smart objects since a central directory is not required to interact with others. We completed an initial implementation of our architecture and demonstrated its feasibility in a case study in the smart home domain.

This paper proposes a solution to performance issues in the selection of Web services based on Quality of Service (QoS) that uses inference mechanisms from Semantic Web resources. Although several researchers highlight the benefits provided by the use of the Semantic Web techniques for searching and compose Web services with QoS, it can become costly when it depends on the approach adopted. Thus, we present a web service selection that uses QoS information in a replicated and distributed ontologies over different service providers. The results point to a significant improvement in the ontology inference process and thus makes the use of semantic resources viable in distributed systems to provide better QoS.

In the Cloud computing community, the calculation of the reputation using the feedback of cloud customers is widely adopted to address the issue of trustworthiness of cloud services. Currently, most methods pursue a global reputation score essentially assuming that the value of a cloud service's reputation is the same for every consumer. However, depending on the expectations and needs of a consumer, there can be significant deviation of perceived reputation for the same cloud service. In this paper we propose a trust management framework that differentiates reputation for various user groups thus providing what we term local reputation. To achieve this we compute the similarity of consumers based a decision-tree model which is used to cluster feedback into localised scores. To refine the result, a time decay factor applicable to feedback is also to be considered. The simulation results illustrate that our approach is feasible and also effective for consumers to choose reputable cloud service.

Using a multi-cloud storage solution requires a user to make complex decisions. Making these decisions can be a problem for regular users who are not familiar with multi-cloud storage. We propose MSSF, a Multi-cloud Storage Selection Framework to automatically select a storage dispersal strategy. MSSF formalises the selection process using a knapsack optimisation problem using integer linear programming along with a rule-based system to select a multi-cloud storage strategy that fits the user needs and requires only simple inputs from the user. Our experiments show the performance and usability aspects of our solution, making it useful in real environments.

Anycast is an effective technique to select system servers among many and by spreading client requests achieve high performance and scalability. In our previous work, we presented the Global Application Layer Anycast (GALA) system. By combining real time network distances measures and geolocation, it performed better than its inherited algorithm, GAA. In this work we analyze how fast the GALA algorithm can find the best server to attend a client request and comparing its convergence to GAA. Using simulations, we show that it converges much faster and we propose a maximum selection time metric to be used in the selection process. Experimental results reveal that the GALA algorithm is two times better than the GAA considering the metric proposed.

Green computing has emerged as a hot topic leading to a need to understand energy consumption of computations. This need also extends to devices with limited resources as are common in the internet of things. RESTful services have shown their potential on such devices, but there are many choices of frameworks for their development and execution. Current research has analysed performance of the frameworks but no attention has been given to systematically studying their power consumption. In this paper we analyse the execution behaviour and power consumption of web services on devices with limited resources and make initial observations that should influence future development of web service frameworks. Specifically, we conduct experiments comparing web services in the Axis2 and CXF frameworks analysing the respective performance and power consumption. Bringing together the best features of small devices and SoC, it is possible to provide diverse, mobile and green applications — however careful selection of development environments can make significant differences in performance and energy consumption.

Access control is concerned with the policies that manage data sharing activities. Access control plays a crucial role in application areas such as education, health and business. However, most programming languages and programming environments do not naturally provide support for implementing access control policies requiring access control policies for systems to be coded as part of the development effort. Access control management policies are high-level features so having to involve a computer programmer during deployment stages for making changes to policies is costly. In this paper, we present an abstraction of access control management policies in the form of extended scrabble in its rules. The needs of access control policies program construct for supporting this game are examined. New relevant program constructs are then incorporated into JACIE (Java-based Authoring language for Collaborative Interactive Environments). The usefulness of these program construct are being demonstrated through the extended scrabble.

This paper presents a semantic approach to custom management of IaaS (Infrastructure as a Service) resources in a cloud computing environment requiring minimal human intervention from both the cloud provider and the user. The proposal differs from other approaches by using autonomic computing and semantic web techniques together to provide a self-configuring and self-optimizing environment that aims to satisfy SLAs (Service Level Agreements). The approach monitors the virtualized resources to guarantee a customized and optimized use based on financial criteria and energy consumption policies.

Fault tolerance techniques can improve the trust of users in service oriented architectures as they can ensure data availability. This paper presents an implementation of a novel fault tolerance mechanism in a SOA architecture which simultaneously provides increased availability and better quality of service. In addition to this mechanism, a service selector using reputation ratings of the architecture components is discussed. The selection is based on information from past transactions of the components of the architecture, which allows to identify the best web services able to meet the requests of customers. The mechanisms are tested and a performance evaluation is presented to validate the results.

The Internet Of Things has emerged, providing an umbrella for the increasing number of heterogeneous Smart Objects that are becoming part of our daily activities. In this scenario, classification approaches are useful to understand differences and identify opportunities of generalization and common solutions, especially when different disciplines are coming together and bringing their individual terms and concepts. We propose a novel model for classifying Smart Objects using capabilities. This five-level model, inspired in the Capability Maturity Models, aims to be simple and inclusive, separating objects with basic capabilities from those with complex ones. In addition, examples of objects for each level are provided as validation of the proposal. The model is useful to identify requirements that Smart Objects have to cover externally as they cannot themselves support them and thus it allows for clear understanding of the external support system (or Middleware) into which the smart object is embedded.

This paper analyzes the execution behavior of web services on devices with limited resources. The experiments compare web services in the Axis2 and CXF frameworks analyzing performance and power consumption. To determine which framework is better suited for service provision, a testing environment and a performance and energy evaluation between them are presented. We show that the Raspberry Pi can be useful in service-oriented applications for different types of tasks. Bringing together the best features of small devices and SoC, it is possible to provide diverse, mobile and green applications.

With an increasing number of companies and individuals adopting cloud computing for their data needs. Naturally, there is a shift in financial and operational costs and and provided services should meet users' performance and cost expectations. We focus on storage and propose MSSF, a Multi-cloud Storage Selection Framework. MSSF contains a basic set of algorithms, a set of security rules and a formal definition of user profiles allowing to fit cloud storage services to user needs. Preliminary experiments investigate the cost differences between two baseline algorithms and user profile models. Considering the promising initial results we provide several observations about the MSSF decision making process that will help with future improvements.

The Internet of Things, large scale sensor networks or even in social media, are now well established and their use is growing daily. Usage scenarios in these fields highlight the requirement to process, procure, and provide information with almost zero latency. This work is introducing new concepts for enabling fast communication by limiting information flow through filtering concepts combined with data processing techniques adopted from complex event processing. Specifically we introduce a novel mediation services architecture using filter policies to reduce latency. The filter policies define when and what data services need to provide to the mediator and thus save on bandwidth. The filter policies describe temporal conditions between two events removing the need to keep a complete history while still allowing temporal reasoning. Promising experimental results highlight the advantages to be gained from the approach.

Web services are developed independently and deployed in a distributed environment, new service can be obtained by composing existing ones. The rapid introduction of new services also results in undesirable interactions between services. These conflicts are not mismatches of interfaces, but are usually based on the data in the executing instance and therefore runtime management of conflicts in Web services should be considered. We study the problem from the perspective of user's revenue, and propose an online approach to resolve conflicts is proposed.

Performing functionality testing in serviceoriented architectures is not a trivial task. The difficulty is especially the large number of components that may be present in a SOA such as brokers, providers, service registries, clients, monitoring tools, data storage tools, etc. Thus, in order to facilitate the process of conducting functional testing and capacity planning in service-oriented systems, we present PEESOS. This first version is a functional prototype that offers facilities to assist researchers and industry to test their new applications, allowing collaborations that can be done between the participants to achieve an appropriate objective when developing a new application. The first results show that it is possible to make a planning environment easier to operate and to readily obtain results for performance evaluation of a target architecture. Since this is a first version of the prototype, it has interface and scalability limitations as well as needing improvements in performance of the logs repository and also in a core engine. We hope that such limitations can be corrected in the near future, including gathering information from the scientific community to make the prototype a useful and accessible tool. PEESOS is on-line and available at http://peesos.wsarch.lasdpc.icmc.usp.br.

Cloud computing is a computing style where resource providers can offer on-demand services in a transparent way and clients usually pay as they go. It introduces a new level of flexibility and scalability for IT users addressing challenges such as the rapid change in IT and the need to reduce cost and time of infrastructure management. However, to be able to offer QoS guarantees without limiting the number of accepted requests, providers must be able to dynamically adjust the available resources to serve requests. This dynamic resource management is not a trivial task, bringing its own challenges related to workload and performance modelling, and deployment and monitoring of applications on virtualised IT resources. An efficient mapping between resources and applications ensures workload balancing and good resource utilization and allows to meet the QoS levels required by clients. This paper presents a performance evaluation that considers different resource configurations in a cloud environment to define which dimension of resource scaling has real impact on client applications.

This paper presents a solution to performance issues in the quality of service aware selection of Web services using techniques of parallelism and mechanisms of inference provided by Semantic Web. The results point to a significant improvement in the speed of searching Web services and thus makes the use of semantic resources viable in distributed systems to provide better quality of service to the clients.

With the rapid development of web service standards and technology, the number of web services on Internet is increasing rapidly. Consequently, discovering the right service to meet a user's requirements quickly and accurately is crucial for the service community. Many web service discovery methods use web service models with semantic descriptions based on ontologies, allowing to apply logical reasoning to the discovery task. However, requiring logical reasoning can lead to sacrifices in efficiency of web services discovery. To address this problem, this paper proposes a combination of ontology encoding with the similarity of information content approach. We encode the concepts in the ontology in a binary encoding in order to improve the discovery efficiency and then we calculate the semantic similarity of information content between services. Validation of efficiency of the proposed approach is conducted through an experiment using the owls-tc2.0 as benchmark test set. The experimental results show that the proposed method not only can improve the efficiency of service discovery, but also can significantly improve the accuracy of service discovery compared with other discovery methods.

Nowadays personalised service provisioning becomes more feasible due to the increasing availability of smart devices, such as smart phones, tablet computers, Personal Digital Assistants and Play stations. These smart devices can dynamically detect the context data and upload them to support other interesting software applications, such as Facebook and Google maps. Context can become richer and more retrievable if links are established for semantically related context data sets. Taking advantage of the recent digital and Web technologies, this paper proposes a novel Linked Context model that applies the Linked Data principles to model and obtain context data from both users and services in one unified framework to support personalised service provisioning at runtime.

Business Processes naturally involve long running activities and require transactional behaviour across them. The work presented in this paper is a proposal for a novel autonomous failure handling mechanism for long running nested transactions (LRT) and forms part of a general management and compensation model for long running transactions in workflows. The mechanism is based on propagation of failures through a recursive hierarchical structure of transaction components (nodes and execution paths). The management system of transactions (COMPMOD) is implemented as a reactive system controller, where system components change their states based on rules in response to triggering of events such as activation, failure, force-fail, completion, or compensation events. A notable new feature of the model is the distinction of vital and non-vital components, allowing the process designer to express the cruciality of activities in the workflow with respect to the business logic.

The cloud concept and its implementations are gaining in importance for systems that connect evermore new devices which in turn require communication with each other. In scenarios where we can find large numbers of data providers on one side and data consumers on the other side, such as in the Internet of Things, large scale sensor networks, machine to machine communication or even in social media, one emerging requirement is to process, procure, and provide information efficiently and with almost zero latency. This work is introducing new concepts to describe the flow of data to and from sources to cloud services in a formal way by limiting information flow with filtering concepts and combining data processing techniques adopted from complex event processing.

Context-aware systems are concerned with identifying the context of a user and then to either provide that information based on queries or to automatically decide on appropriate actions to be taken. Some context aspects (such as location) are easy to sense through hardware, while the activity of a user has shown to be somewhat elusive to being sensed with hardware sensors. As users use web services more frequently they are exchanging messages with the services through the SOAP protocol. SOAP messages contain data, which is valuable if gathered and interpreted right -- especially as this data can be shedding information on the activity of a user that goes beyond "sitting at the computer and typing". We have developed software sensors, essentially based on monitoring SOAP messages and inserting data for further reasoning and querying into a semantic context model. In this paper we consider a solution to map the data from a SOAP message to our OWL ontology model automatically. Specifically, we explain the methodology to map from SOAP messages to an existing structure of knowledge.

Service-Oriented Computing is benefit of interoperation among services. Current service-oriented computing research is much more concerning the low level interoperation among services, such as service discovery, service composition etc. However, the high level research issue-the feature interaction problem is also challenging the interoperation of service-oriented computing. Traditional feature interaction methods are focused on the service design phrase with formal methods or software engineering analysis. Autonomy and distribution of service deploying style have made the needs of runtime detecting and resolving feature interaction in SOC research community. This paper investigates the detection of feature interactions in web services at runtime and proposes ESTRIPs, an extended STRIPS operation conflict-free of services in business process detection method, which reasons from OWL-S and SWRL combined with runtime SOAP messages. First, we give the model of the feature interaction problem in business process during its execution and then the ESTRIPS method given in detail. The implementation of a prototype is illustrated. Using a real world scenario shows the plausibility of our method of detecting feature interactions of business process.

With an increasing use of services sustaining the resources which people need has become more important. In this paper, we propose an effective reservation method, called “BPSR” (Business Process Service Reservation), which aims for the process reservation which to the best of our knowledge has not been studied before. In particular we address for major jobs: service differentiation; service reservation; process reservation and QoS Control. We also describe a flexible policy-based reservation method which aims to increase the success rate of reservation and utilization of service resources. Experimental analysis shows that the BPSR reservation system achieves better results than other reservation methods.

Feature interaction has been identified as a problem in the telecommunications domain in the 1980s, but since it has been shown to be a problem of systems that are composed of individually designed components. Clearly Web service composition is a way of building services from independently designed components and hence is subject to the same problem. This paper investigates the detection of feature interactions in Web services at runtime and proposes a novel detection method by taking inspiration from the Situation Calculus. Two case studies show that it is effective for detecting feature interactions in composite Web services.

Automatic Web services composition can be achieved by using AI planning techniques. HTN planning has been adopted to handle the OWL-S Web service composition problem. However, existing composition methods based on HTN planning have not considered the choice of decompositions available to a problem which can lead to a variety of valid solutions.In this paper, we propose a model of combining a Markov decision process model and HTN planning to address Web services composition. In the model, HTN planning is enhanced to decompose a task in multiple ways and hence be able to find more than one plan,taking both functional and non-functional properties into account. Furthermore, an evaluation method to choose the optimal plan and some experimental results illustrate that the proposed approach works effectively.