Dynamic selection mechanism for quality of service aware web services

With the proliferation of Web services as a business solution to enterprise application integration, ranking and selecting the best Web services among the providers become an important factor in the success of the business solution. Quality of service (QoS) determines the quality and usability of a service including its price, performance, reliability, integrity, accessibility, availability, interoperability, and security. Given a set of QoS attributes from a variety of sources, it is a challenge to sort through all of them and be able to get the best services that meet QoS requirement. In this paper, we describe a novel method by which Web services can be ranked and selected automatically based on a number of observed QoS parameters and feedback responses learned from prior knowledge. This new approach treats the observed Web services QoS attributes and target Web services relationship, represented by a matrix, as a statistical problem. Using singular value decomposition (SVD) technique, and an user assisted weighting system, implicit higher order correlations among Web services and their associated QoS attributes are extracted and used to estimate the selection of recommended Web services.

With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. With the increasing number of web services having equivalent functionality, the binding procedure is driven by some non-functional, Quality of Service (QoS) criteria, such as the money cost, response time, reputation, reliability or a trade-off between them. Thus, an important problem is, given QoS constraints, how to aggregate and leverage individual service’s QoS information to derive the optimal QoS of the composite service. In this paper, we propose a novel QoS model for performing flexible service selection. The key idea of the model is to relax users’ QoS constraints and try to find the most possible services satisfying users’ QoS requirements. Based on the proposed QoS framework, we develop various algorithms for making service selection on individual and composite services. We also introduce a top-k ranking strategy to reflect a user’s personalized requirements. Experimental evaluation shows the proposed QoS model is efficient and practical.

Web services provide a promising solution to an age old need of fast and flexible information sharing among people and businesses. One of the key research issues in web services is selection of most pertinent web services. Selection of web service has become a tedious job because of the increasing number of service providers providing services with similar functionality. Therefore, a framework for selection and discovery of web service that can meet the non-functional requirements is needed. In this work, we have developed a selection tool (WSS-NFP) for web services which can rank services based on their non functional properties such as performance, delay etc. We have used soft computing techniques for selection and discovery of web services based on non functional properties. These properties are fuzzy in nature. A tool is developed and implemented that is generic in nature and can be customized for any application domain. As a core of this tool, it consists of Fuzzy System for ranking web services and Neural Network for fine tuning the membership functions for linguistic terms. Appropriate and fine tuned membership functions help in minimizing the output error measure and maximizing the performance index of the proposed tool. This tool is basically a Neuro-Fuzzy system for ranking web services of any domain.

While Web service models become increasingly popular and many service providers start to offer all kinds of Web services, the quality of service (QoS) for Web services has become an important factor for service requesters to consider during the selection process of appropriate Web services. In this paper, we present an evaluation and selection framework (CosmosQoS) for QoS-required Web services. A novel architecture and reputation appraisal model to monitor, store and evaluate the physical usage and post-condition of requested Web services in a requester-located network is proposed to better support service requesters such that they can select suitable Web services based on their QoS requirements, trustworthiness of Web services, and effect of network connection and configuration between service requesters and providers.

The growing number of Services on the Web has made locating desired Web Services a sizable challenge. Web Service requestors deem a Quality of Service (QoS) based Web Service selection important in terms of providing a relevant and user centric service selection experience. In this thesis an interactive QoS based Web Service browsing mechanism is proposed, which makes use of three clustering algorithms including vector-based, preference-based and weighted clustering. We use symbolic interval data as the principle representation of QoS attributes. The browsing mechanism which was implemented as part of this research allows service requestors to prioritize their search by hierarchically clustering their web services. This is done in order of their preferences and also by attaching a weight to each QoS attribute, which is a beneficial compromise between performance-high preference-based clustering and time-efficient vector-based clustering, Along with several extensive experiments, a user study was conducted in order to test the usability of this browsing mechanism and to test the overall efficiency and performance of the three clustering algorithms in comparison. The result of the experiment led to evidences that preference-based browsing approach was the most efficient one when compared to vector-based or weighted clustering approaches.

Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-complete. The search time is mainly dependent on the number of requested tasks, the size of the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the task services and a bat algorithm metaheuristic for selecting the final near-optimal compositions. The fitness used by the metaheuristic aims to fulfil all the global constraints of the user. The experimental study showed that the ranking heuristics, termed “fuzzy Pareto dominance” and “Zero-order stochastic dominance”, are highly effective compared to the other heuristics and most of the existing state-of-the-art methods.

Virtual enterprise is a temporary network of independent companies or enterprises that can quickly bring together to fulfill a value-added task. With the development of Web service technologies, the enterprise business systems can be encapsulated as Web services. Establishing a virtual enterprise is actually a process of Web services composition. As more and more Web services are available, many Web services with different quality of service (QoS) can be found for tasks of the composite Web service. Thus, a significant research problem in Web service composition is how to select the correct Web services for tasks such that the composite Web service gives the best overall quality. In this paper, we propose an approach to achieve Web services selection efficiently with excellent comprehensive quality. Firstly, a comprehensive evaluation model based on generic QoS (GQoS) and domain QoS (DQoS) of composite Web service is established. Secondly, a novel optimization algorithm named culture max–min ant system (C-MMAS) is constructed to solve the problem of Web service selection based on DGQoS. Simulations show that when used in Web service selection, C-MMAS is better than ant colony optimization algorithm and MMAS in efficiency.

The web service has widely spread in recent years which is showed by the large number of published web services. However, users may not always find their needs in one web service which has consequently spawned a composition of web services. This composition lead to many task difficulties including the selection of web services, which also consists of choosing the best QoS-based services among other similar services provided. From this perspective, there are several approaches to the selection of web services, however, this remains deficient at the level of execution time due to the rage number of competing web services. In this paper, we propose a new selection approach based on prefiltering and Skyline, this approach is tested and improved using a real dataset. We used two steps to solve this problem, prefiltering and Skyline. The first step is the use of K-Means clustering to bundle web services with similar Quality of Service (QoS) properties under one umbrella is explored. The second one is defining the most dominant web service using the Branch-and-Bound Skyline algorithm.

With the increasing popularity of deploying web services, quality of service (QoS) becomes a significant concern for service consumers and providers for service selection. However, representing and storing the values of QoS attributes are problematic, as the current universal description, discovery and integration (UDDI) was not designed to accommodate these emerging requirements. To solve the problems of storing QoS in UDDI and aggregating QoS values, three different approaches, namely type, keyword based and ontological approaches to model QoS tModel (technical model) have been proposed. The aim is to study these approaches and other existing QoS tModel representation for their efficiency and consistency in service selection. In this paper, we also report a number of experimental results based on an example in order to evaluate these approaches.

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.

Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.

A broker-based quality evaluation system for service selection according to the QoS preferences of users

Aim at the inefficient problem of existing QoS-based Web service selection approach, the paper defines the marks of new Web service to distinguish between current and new. It is an improvement of existing QoS-based Web service selection approach based on this paper. The range of Web Services selection has been limited, namely the best quality of Web Services are selected from the new Web Services only. So the range of selection has been reduced and the efficiency has been improved of Web Services selection.

With the rapid growth of abundant Web services on the World Wide Web, designing effective approaches for Web service selection, which satisfy service consumers’ customized quality of service (QoS) requirement, has become a fundamental problem for the application of composed Web service. To address this problem, we propose, in this paper, a new algorithm, called Mixed Intelligent Optimization Algorithm (MIOA). MIOA optimizes service selection with multiple QoS constraints by taking advantage of both Maximum Entropy Method and Social Cognitive Optimization theory. Furthermore, Chaos method is also integrated into MIOA to improve the existing social cognitive optimization algorithm. Extensive analysis and simulations have been conducted. Our experimental results show that MIOA effectively selects high-quality service, and its convergence is also improved. Key words: Web service, quality of service, service selection.

Selecting high quality web services based upon QoS(Quality of Service) is a significant task in service-oriented computing environments. In this paper, according a real Quality of Web Service data sets:QWS Dataset (1.0), we developed a Weighted Grey Relational Analysis Model(WGRAM) for web service selection based on QoS, and combined with Analytic Hierarchy Process(AHP) and Grey Relational Analysis (GRA) to prioritize the relative importance of the QoS of web services. The comprehensive evaluation model (WGRAM) fully considered the influence of many factors given by experts, incorporating the qualitative analysis of the index of various factors and the quantization for scientific decision-making, it can provide weighted grey relational degree for each Web Services with the same functions(operations), and the grey relational sequence which can guide the optimal service selection from multiple functions similar service. The model possessed the characteristic of simple calculating process, and the result was reliable, It’s a reliable and effective method of web service quality factor assessment and a significant attempt for web services selection.