EFFICIENT APPROACH FOR WEB SERVICES SELECTION WITH MULTI-QoS CONSTRAINTS

A natural language collaborative consultation system must take user preferences into account. A model of user preferences allows a system to appropriately evaluate alternatives using criteria of importance to the user. Additionally, decision research suggests both that an accurate model of user preferences could enable the system to improve a user's decision‐making by ensuring that all important alternatives are considered, and that such a model of user preferences must be built dynamically by observing the user's actions during the decision‐making process. This paper presents two strategies: one for dynamically recognizing user preferences during the course of a collaborative planning dialogue and the other for exploiting the model of user preferences to detect suboptimal solutions and suggest better alternatives. Our recognition strategy utilizes not only the utterances themselves but also characteristics of the dialogue in developing a model of user preferences. Our generation strategy takes into account both the strength of a preference and the closeness of a potential match in evaluating actions in the user's plan and suggesting better alternatives. By modeling and utilizing user preferences, our system is able to fulfill its role as a collaborative agent.

In cloud computing, there exist several classes of web services, and each class of web service contains multiple services which have different Quality of Service (QoS) values. To provide QoS for users, determining the best service composition that meet user's preferences is necessity. Finding the best service composition needs a certain method in order to provide the best combination of QoS with minimum response time, while in some cases, the process is bounded by budget constraint provided in user's preferences. In a simple method, for selecting the best service composition, we need to evaluate nm combinations of n classes of service and m services member. This condition leads to a large computing costs and memory consumption that occur during the process of generating the combination of data. To reduce the number of combinations, we propose an approach for finding the best service composition of web services using skyline algorithm which can adapt different user preferences of QoS. While selecting the best composition, we also take consideration in budget as constraint by selecting the closest user's budget with the best skyline result of services. SalSa skyline algorithm have given the new alternative ways to finding the best candidates of services in each class of service in reasonable time. These happened because we only need to evaluate the smaller parts of members in each class which will need much time if the number of services is plenty. Moreover, we also presented a method to find the best composition of services which able to present the composition that meet the user's budget.

The article describes a method for developing and modelling composite web-services. Web-service composition is used to derive new functionality from the interaction of existing web-services. Composite web-services are built in several stages: specifications (determining the type of service); development of the structure of the service based on the algebra of services; service composition modelling; selecting a service variant and generating a service. The main elements of the proposed approach are the algebra of services and web-services interaction models (basic and composite). The above approach formally presents the consideration of the main aspects in solving problems related to the construction of effective composite web-services and the selection of mathematical models, namely: description of web-services, determining the structure of web-services, taking into account the dynamics of information changes, taking into account the main uncertainties in building the structure web-services. Based on the approach, a method for constructing composite web-services has been developed. The method is based on an algebraic description of a web-service based on a specialized algebra of services. The method consists of the following steps: description of the task of developing a web-service; development of the structure of a web service based on the algebra of services; formal description of the structure and clarification of service operations; building models of functioning and interaction of service components; building simulation models of the composite service; defining the final structure of the composite service; web-service implementation. The main tool for describing composite services is the algebra of services, and the model building tool is colouredPetri nets implemented using СPN Tools. As an example of the application of the developed method, the construction of a pharmaceutical service is described, in which the modellingof the interaction of web-services based on Petri nets is used. The process of building a composite model of a web-service is considered. The process begins with building a pharmaceutical web-service model structure based on the service algebra. Based on the service algebra, operators were developed to implement composite services. The structure of the model is developed, which is presented in the form of a formal description. The process of building pharmaceutical service models in the СPN Tools environment is described in detail.

The QoS of web service has been increasingly crucial due to the escalating number of services with similar or identical functionality, which leads to intensive researches on QoS-aware web service composition. Correspondingly, to optimize not only QoS but also service quantity in a composition has also been increasingly challenging. Currently, there are already many researches on service composition addressing the optimization of multiple QoS attributes, but it is still rare to take service quantity as an optimization objective as well. To address this issue, this paper proposes a novel supervised web service composition mechanism integrating multi-objective QoS optimization and the minimization of service quantity. Firstly a memory-based search algorithm is proposed to compute each single-objective optimal QoS, after which a knapsack-variant algorithm is applied to minimize the number of services without considering the QoS. Finally, a supervised multi-objective optimization is performed based on the above single-objective optimization results. Experimental results on both Web Service Challenge 2009’s datasets and substantial datasets randomly generated show that the proposed service composition method outperforms the state-of-the-arts by achieving a much better tradeoff among all the objectives.

The Business Process Execution Language (BPEL) has become the standard in Web service composition, so that been adopted by industries to create mission critical services. The business process reliability especially business process consistency when some services failed is critical to composite services especially involving various organizations. However, it is difficult to conduct business process reliability testing for composite services due to complex business process in service composition, remote deployed services and long time testing duration. Little research is done to test fault tolerance capabilities of composite services only from low level. This paper proposes a technique on how to test the reliability of composite service defined in BPEL from the view of business semantics with little cost using fault injection. We present an approach for reliability testing of web services by using service stubs with semantic faults instead of real services which can be placed at service provider side or service consumer side. Dislike existing studies, we focus on how to find the reliability problems associating with business process - called semantics as the problems are not pure coding error but faults related to business process. In addition, the behavior of composite services in BPEL is analyzed when there are faults in orchestrated services invoked. Finally, a case study is given to show the whole process of reliability testing for composite web service.

The automatic composition of Web services requires the existing services to be encoded in a semantic form, such as ontology. If Web services are represented with ontology, we can automatic compose Web services by means of their semantics. Ontology modeling is divided into two steps: input/output parameters modeling and Web services modeling. First, we collect Web services' input/output parameters, represent them with classes in ontology, and transform Web services to properties in ontology, define relations between classes and properties. Then we judge Web services' relations based on relations between properties transformed from these Web services and estimate whether these Web services can be composed.

A High-level Petri Net Based Approach for Modeling and Composition of Web Services

Managing Service Level Agreement (SLA) for composite semantic web services is a very complex process, as it involves many complex tasks such as components discovery, provisioning, monitoring, recovery, and coordination. Indeed, managing all these tasks manually is a very cumbersome operation, nevertheless it is time consuming and prone to errors. To overcome such problems, automated SLA management for composite semantic web services is needed. This requires the SLA of the composite service to be described in a machine-understandable format such that it can be understood and processed by the automated SLA management tool. Currently, there is no SLA description language for composite semantic web services, but there exists an SLA description language for atomic web services, known as the Web Service Level Agreement (WSLA). Hence, in this paper, we aim to extend the WSLA language to describe composite semantic web services' SLAs, then we show how we can automatically generate the components' SLAs from the composite semantic web services' SLAs; adopting the CRECENT framework that we previously proposed for managing composite web services.

Service Oriented Architecture (SOA) introduced the web services as distributed computing components that can be independently deployed and invoked by other services or software to execute composite services that represent an end-to-end business process. An important problem in the SOA is the web service selection and composition problem which involves selecting the right web services mix to construct the best service composition that achieves the consumers Service Level Agreement (SLA) requirements. In this chapter we propose different methods to solve the problem of the business processes execution engine web service selection and services composition construction in the Service Oriented Architecture (SOA).  The chapter provides different mechanisms to improve the web services selection and composition using dynamic techniques. The main goal is to satisfy the services' requirements expressed as the SLA and to enhance the services selection and composition by increasing the availability and decreasing the response time of the service compositions.

Cloud computing has forced providers to give satisfying services to users. Unfortunately, there is no model that integrates user preferences with services offered by providers. The services provided are subject to change and thus this information must be reflected in the proposed dynamic model so the user can make an up-to-date decision in choosing the provider based on his preferences. In order to construct and evaluate this dynamic infrastructure as a service (DIAAS) model, the services considered are the speed of central processing unit (CPU), the size of random-access memory (RAM), the size solid-state drive (SSD), the bandwidth in bits per second (bit/s), and the cost of service. The DIAAS uses intelligent tool (ITOOL) for grabbing current provider functional services and stores user preferences. ITOOL retrieves the values of services either by using Web Services or JSON. The services are weighted using linear equations and ranked using average sum of the weighted services. DIAAS will display the list of providers according to user preferences after performing weighting and ranking procedures. There are changes in the value of services by providers, and this implies that user has to be aware of these changes. DIAAS can also be used by providers to improve their services. The findings of DIAAS model will be provided based on three levels (Low, Medium, High). Low = 33.33%, Medium = 6.66%, High = 100%. In DIAAS model, the low percentage will be given to the lower weight of the service and the high percentage of highest weight. Except for cost, the low percentage will be given to highest weight and the high percentage of lower weight. After that, we calculate the weight of each service for each provider by the linear equation formula. Finally, the rank value for each provider is the average of the summation of weights for all the services.

A web service is a software system that supports interoperable machine to machine interaction over the network. It can be any kind of service provided on the web that can exchange data between applications. In simple terms, it is any program that is callable by another program across the web using standard protocols. Web services are special because they are independent of the platform, programming language, and model design. In current web applications, more businesses are gradually publishing their business as services over the web. This growing number of web services available within an organization and on the Web raises a new and challenging search problem: locating desired web services. Searching for web services with conventional web search engines is insufficient in this context. In this paper, a latent semantic web service discovery and composition framework, addressing both discovery and composition of web services, is introduced. The framework provides a graphical visualization interface that can facilitate users in finding the desired web service beyond the general keyword search and provide composition of complex services if the service does not exist. A novel approach based on an information retrieval technique known as latent semantic analysis is applied to a large web service descriptions data set collection. By using co-occurrence patterns across the entire data collection, and then using those patterns to infer semantic relationships between documents, relevant results in a ranked order can be retrieved. Moreover, a back to front algorithm is applied to the composition search interface in order to compose complex web services in full or partial terms from pre-existing web services when the requested service does not exist. An experimental study conducted on a collection of 2525 publicity available web service shows improved performance to the technique applied in obtaining much better search results.

Smart home which understands user's preference and provides right services at right time is the current trend. In this paper, we aim at developing a system which can achieve this objective by using the Bayesian network to model user's preference. Instead of assuming the structure of Bayesian network is invariant, our system interacts with user appropriately to obtain some useful information and we use the semi-supervised learning with these information to both learn and adjust the Bayesian network for modeling the user's preference in a more accurate manner. We can use preference model to provide adequate service in home environment. A simulation and a real home environment are constructed based on the proposed method, and the experiments also show the usefulness.

Trust is one of the most critical quality factors for service requestors when they select the services from a large pool of Web services. However, existing web service trust models either do not focus on satisfying user's preferences for different quality of service (QoS) attributes, or do not pay enough attention to the impact of malicious ratings on trust evaluation. To address these gaps, a user-oriented trust model considering user's preferences and false ratings is proposed in this paper. The model introduces an approach to automatically mine user's preferences from their requirements, the preferences are used to determine the weights of each QoS attribute when integrating local trust into the multi-dimensional QoS attributes. The local trust on a service for the user is derived by combining the trust on QoS attributes and the trust on user's ratings. In this model, the users are classified into different groups according to their preferences, the honesty of each group is assessed by filtering out dishonest users using a hybrid approach which combines rating consistency clustering and an average method. To calculate the global trustworthiness of a service for the users group, the weight of ratings is dynamically adjusted according to the results of honesty assessment. The simulation results indicate that the model works well on personalized evaluation of trust, and it can effectively dilute the influence of malicious ratings.

Web service composition enables the creation of new and more valuable services to combine and link existing services. However, the treatment of user constraints (as user demands) is important to provide users with a more intelligent service. It is difficult for existing services to fully satisfy' users' constraints, as user demands change dynamically, depending on user preference or environment. We have produced an intelligent infrastructure that can deal with dynamic user constraints for Web service planning and composition. To realize this intelligent system, we analyzed the pattern of exceptions to manage the user constraints that can occur during planning through the creation of a scenario, and then we extracted important ideas for the detection and improvement of the exceptions. We also considered the system architecture using exceptional patterns and situation calculus theory in the first-order logic-language to deduce the planning procedure. Our simple prototype used IndiGolog, which is a high-level programming language for robot, and applied situation calculus theory. Using this intelligent infrastructure enabled us to easily design a complex plan.

Automatic web service composition is defined as the const ruction of virtual enterprises as complex services on demand. In the current research scenario choosing business service partners for composition on the fly is usually determined with user-centric metrics and deficient of provider’s business specific metrics. Automatic web service composition in today’s real world business circumstances is more of a toy model due to the lack of service provider collaboration metrics [1]. SLAKY System is a new model for selection of business service partners by adding servic e provider metrics to user centric metrics enabling strategic and realistic selection of services for composition. SLAKY System selects services automatically considering the vision, time planning, environmental context, user adoption, usage policies, trus t management, risk management, market scenario, native intelligence, and competitive profit management as service provider collaboration metrics in addition to functionality satisfaction metrics for client’s requirements. The time planning metric was designed using opus deviser algorithm [2] and profit management using SLAKY BWG algorithm [3]. In this paper we focus on strategic selection of semantic services based on the metric of native intelligence. Howard Gardner, psychology professor, Harvard Universit y, defines intelligence as an ability to solve problems, or to create products, that are valued within one or more cultural settings. Richard Heck, Professor of Natural Theology, Brown University, in his work states that native intelligence is primarily influenced by racial and cultural differences. User preferences in any sector intend to have native influence. For example the affinity towards entertainment services for Indian Premier League or low cost car booking service for TATA Nano is more in India th an America. Moreover decision made by a decision maker is motivated by native intelligence in addition to Intelligence Quotient and is valued highly in native grounds. For example the selection of a matrimonial service totally relies on native aspects. Native Intelligence is also a significant metric to improve time -to-market and quality in the launch of any product. Considering the significance of native intelligence in strategic selection we layer native intelligence descriptions for realistic selection o f semantic web services on the fly. OWL -S upper ontology that describes semantics of services for automation of service discovery, selection, composition, orchestration and invocation lacks entities for semantic modeling of native intelligence. In this pap er we propose an extended OWL -S ontology which augments where, why and who native status descriptions of a service apart from the existing what (ServiceProfile sub -ontology) and how (ServiceModel, ServiceGrounding sub-ontology) semantics. The why, whereand who semantics are newly augmented as ServiceParameters of Profile sub -ontology using sParameter property. We also propose Naavi algorithm that reasons with common sense knowledge for selection of the web service that is highly valuable in a native context using native description in extended OWL -S.