A Framework Recommending Top-k Web Service Compositions: A Fuzzy Set-Based Approach

International audienceData Web services allow users to access information provided by different companies. Web users often need to compose different Web services to achieve a more complex task that can not be fulfilled by an individual Web service. In addition, user preferences are becoming increasingly important to personalize the composition process. In this paper, we propose an approach to compose data Web services in the context of preference queries where preferences are modelled thanks to fuzzy sets that allow for a large variety of flexible terms such as "cheap", "affordable" and "fairly expensive". Our main objective is to find the top-k data Web service compositions that better satisfy the user preferences. The proposed approach is based on an RDF query rewriting algorithm to find the relevant data Web services that can contribute to the resolution of a given preference query. The constraints of the relevant data Web services are matched to the preferences involved in the query using a set of matching methods. A ranking criterion based on a fuzzyfication of Pareto dominance is defined in order to better rank the different data Web services/compositions. To select the top-k data Web services/compositions we develop a suitable algorithm that allows eliminating less relevant data Web services before the composition process. Finally, we evaluate our approach through a set of experiments

SLA based cloud service composition using genetic algorithm

Cloud computing tends to provide high quality on-demand services to the users. Numerous services are evolving today. Functionally similar services are having different non-functional properties such as reliability, availability, accessibility, response time and cost. A single service is inadequate for constructing the business process. Such business process is modeled as composite service. Composite service consists of several atomic services connected by workflow patterns. Selecting services for service composition with the constraints specified in Service Level Agreement is the NP-hard problem. Such a cloud service composition problem is modeled in this paper. Genetic based cloud service composition algorithm (GCSC) is proposed. Proposed algorithm is compared with the existing genetic based cloud service composition algorithm based on average utility rate and convergence time. It is proved that the proposed algorithm provides better performance as compared to the existing cloud service composition algorithm

A Review on Framework and Quality of Service Based Web Services Discovery

Selection of Web services (WSs) is one of the most important steps in the application of different types of WSs such as WS composition systems and the Universal Description, Discovery, and Integration (UDDI) registries. The more available these WSs on the Internet are, the wider the number of these services whose functions match the various service requests is. Selecting WSs with higher quality largely depends on the quality of service (QoS) since it plays a significant role in selecting such services. In achieving this selection of the best WSs, the potential WSs are ranked according to the user’s necessities on service quality. In many cases, the value of QoS ontology is realized by its support for nonfunctional features of WSs. This ontology is also capable of providing solutions to the interoperability of QoS description. Moreover, based on the QoS ontology, it becomes more possible to develop a framework of semantic WS discovery. The framework enhances the automatic discovery of WSs and can improve the users’ efficiency in finding the best web services. Thus, Web Services are software functionalities publish and accessible through the Internet. Different protocols and web mechanism have been defined to access these Services

Methods and algorithms for service selection and recommendation (preference and aggregation based)

In order for service users to get the best service that meets their requirements, they prefer to personalize their non-functional attributes, such as reliability and price. However, the personalization makes it challenging because service providers have to deal with conflicting non-functional attributes when selecting services for users. In addition, users may sometimes want to explicitly specify their trade-offs among non-functional attributes to make their preferences known to service providers. Typically, users\u27 service search requests with conflicting non-functional attributes may result in a ranked list of services that partially meet their needs. When this happens, it is natural for users to submit other similar requests, with varying preferences on non-functional attributes, in an attempt to find services that fully meet their needs. This situation produces a challenge for the users to choose an optimal service based on their preferences, from the multiple ranked lists that partially satisfy their request. Existing memory-based collaborative filtering (CF) service recommendation methods that employ this recommendation technique usually depend on non-functional attribute values obtained at service invocation to compute the similarity between users or items, and also to predict missing non-functional attributes. However, this approach is not sufficient because the non-functional attribute values of invoked services may not necessarily satisfy their personalized preferences. The main contributions of this work are threefold. First, a novel service selection method, which is based on fuzzy logic, that considers users\u27 personalized preferences and their trade-offs on non-functional attributes during service selection is presented. Second, a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user is also presented. Two algorithms were proposed: 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Finally, a CF-based service recommendation method that considers users\u27 personalized preference on non-functional attributes if proposed. Examples using real-world services are presented to evaluate the proposed methods and experiments are carried out to validate their performance --Abstract, page iii

Semantic Constraint and QoS-Aware Large-Scale Web Service Composition

Service-oriented architecture facilitates the running time of interactions by using business integration on the networks. Currently, web services are considered as the best option to provide Internet services. Due to an increasing number of Web users and the complexity of users’ queries, simple and atomic services are not able to meet the needs of users; and to provide complex services, it requires service composition. Web service composition as an effective approach to the integration of business institutions’ plans has taken significant acceleration. Nowadays, web services are created and updated in a moment. Therefore, in the real world, there are many services which may not have composability according to the conditions and constraints of the user's preferred choice. In the proposed method for automatic service composition, the main requirements of users including available inputs, expected outputs, quality of service, and the priority are initially and explicitly specified by the user and service composition is done with this information. In the proposed approach, due to a large number of services with the same functionality, at first, the candidate services are reduced by the quality of service-based Skyline method, and moreover, by using an algorithm based on graph search, all possible solutions will be produced. Finally, the user’s semantic constraints are applied on service composition, and the best composition is offered according to user’s requests. The result of this study shows that the proposed method is more scalable and efficient, and it offers a better solution by considering the user’s semantic constraints

Full Solution Indexing and Efficient Compressed Graph Representation for Web Service Composition

Service-oriented computing enhances business scalability and flexibility; providers who expect to benefit from it may bring explosive growth of web services. Searching an optimal composition solution with both functional and non-functional requirements is a computationally demanding problem: the time and space requirements may be infeasible due to the high number of available services. In this thesis, we study QoS-aware service composition problems which satisfy functional requirements as well as non-functional requirements. We use optimization algorithms to enhance accuracy of our searching algorithms. In the first approach, we propose a database-based approach to search a service composition solution. Current in-memory methods are limited by expensive and volatile physical memory, to deal with this problem, we want to use the large space available in relational database on persistence disk. In our database-based approach, all possible service combinations are generated beforehand and stored in a relational database. When a user request comes, SQL queries are composed to search in the database and K best solutions are returned. We test the performance of the proposed approach with a service challenge data set; experiment results demonstrate that this approach can always successfully find top-K valid solutions.We offer three main contributions in this approach. First, we overcome the disadvantages of in-memory composition algorithms, such as volatile and expensive, and provide a solution suitable to cloud environments. Second, we fetch top-K solutions in case the optimal solution is not available as backup solutions to the user. Third, compared with other pre-computing composition methods, we use a single SQL query: there is no need to eliminate spurious services iteratively. Then, we propose the application of a skyline operator to reduce the search space and improve the scalability. Skyline analysis returns all of the elements that are not dominated by another element. We use skyline analysis to find a set of candidate services referred to as "skyline services", therefore, less competitive services are reduced. This allows us to find a solution for a large composition problem with less storage and increased speed. In reality, different users may have same requests, we are motivated to pick some popular requests and generate paths for fast delivery. These paths are stored in a separate table of the relational database. When a user request comes, we first search to find a nearly ready-made solution. Only as a last resort do we search the table with whole paths to find a solution. Finally, to deal with the problem that the search space may explore, we apply a compressed data structure to represent the service composition graph. The goal is to allow algorithms running in in-memory over larger graphs. In this approach, we use compact K2-trees to represent the service composition graph. When a user request comes, we search the K2-tree for a satisfactory solution. We use an array to store values in the last level of the compact tree, which represents relationships between services and concepts. In our algorithms, we find services' inputs (resp. outputs) by locating elements in this array directly, therefore, decompressing the graph is unnecessary. To the best of our knowledge, our work is the first attempt to consider compact structure in solving web service composition problems. Experiment results demonstrate that this approach takes less space and has good scalability when handling a large number of web services. We provide different approaches to search a solution for the user. If the user want to find an optimal solution with fewer services, he may use the database-based approach to search for a solution. If the user want to get a solution in a short time, he may choose the in-memory approach

Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model

The functionally equivalent web services (WSs) with different quality of service (QoS) leads to WS discovery models to identify the optimal WS. Due to the unpredictable network connections and user environment, the predicted values of the QoS are likely to fluctuate. The proposed Context-Aware Bayesian Network (CABN) system overcomes these limitations by incorporating the contextual factors in user, server, and environmental perspective. In this paper, three components are introduced for personalized QoS prediction. First, the CABN incorporates the pre-clustering model and reduces the searching space for QoS prediction. Second, the CABN confronts with the multi-constraint problem while considering the multi-dimensional QoS parameters of similar QoS data in WS discovery. Third, the CABN sends the normalized QoS value of records in similar as well as neighbor clusters as inputs to the Dynamic Bayesian Network and improves the prediction accuracy. The experimental results prove that the proposed CABN achieves better WS-Discovery than the existing work within a reasonable time

Service recommendation and selection in centralized and decentralized environments.

With the increasing use of web services in everyday tasks we are entering an era of Internet of Services (IoS). Service discovery and selection in both centralized and decentralized environments have become a critical issue in the area of web services, in particular when services having similar functionality but different Quality of Service (QoS). As a result, selecting a high quality service that best suits consumer requirements from a large list of functionally equivalent services is a challenging task. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase of service consumers and a consequent diversity in Quality of Service (QoS) available. Increases in both sides leads to a diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. Furthermore, it is challenging for customers to select suitable services from a large number of services that satisfy consumer functional requirements. Therefore, web service recommendation becomes an attractive solution to provide recommended services to consumers which can satisfy their requirements.In this thesis, first a service ranking and selection algorithm is proposed by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. With the initial list of available services the approach considers those services with a partial match of consumer requirements and ranks them based on the QoS parameters, this allows the consumer to select suitable service. In addition, providing weight value for QoS parameters might not be an easy and understandable task for consumers, as a result an automatic weight calculation method has been included for consumer requirements by utilizing distance correlation between QoS parameters. The second aspect of the work in the thesis is the process of QoS based web service recommendation. With an increasing number of web services having similar functionality, it is challenging for service consumers to find out suitable web services that meet their requirements. We propose a personalised service recommendation method using the LDA topic model, which extracts latent interests of consumers and latent topics of services in the form of probability distribution. In addition, the proposed method is able to improve the accuracy of prediction of QoS properties by considering the correlation between neighbouring services and return a list of recommended services that best satisfy consumer requirements. The third part of the thesis concerns providing service discovery and selection in a decentralized environment. Service discovery approaches are often supported by centralized repositories that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes and previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of suitable web service by taking into account the QoS properties of the web services. The major contribution of the thesis is providing a comprehensive QoS based service recommendation and selection in centralized and decentralized environments. With the proposed approach consumers will be able to select suitable service based on their requirements. Experimental results on real world service datasets showed that proposed approaches achieved better performance and efficiency in recommendation and selection process.N/