Efficient Retrieval of Web Services Using Prioritization and Clustering

WEB services are software entities that have a well defined interface and perform a specific task. Typical examples include services returning information to the user, such as news or weather forecast services. A web service is formally described in a standardized language (WSDL). The service description may include the parameters associated with web services like input , output and quality of service. As web services and service providers proliferate, there will be a large number of candidate, and likely competing, services for fulfilling a desired task. Hence, effective service discovery mechanisms are required for identifying and retrieving the most appropriate services. The main contributions of our paper are summarized as follows; we propose and implement a method for determining dominance relationships among service advertisements that simultaneously takes into consideration multiple PDM criteria. We introduce a method for prioritization and clustering web services based on similarity measures using efficient algorithms Keywords : Web Service , PDM , dominance score ,TKDD, clustering

Fuzzy Optimised Power Generation from Moving Vehicles

Abstract In our Paper regenerative power system for electric motorcycles and cars that performs regenerative energy recovery from the axle of the vehicle based on fuzzy logic control for a boost converter is used to boost (maintain) the voltage level. Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. A constant regenerative current control scheme is proposed, thereby providing improved performance and high energy recovery efficiency at minimum cost. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities

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

Modeling and Selection of Software Service Variants

Providers and consumers have to deal with variants, meaning alternative instances of a service?s design, implementation, deployment, or operation, when developing or delivering software services. This work presents service feature modeling to deal with associated challenges, comprising a language to represent software service variants and a set of methods for modeling and subsequent variant selection. This work?s evaluation includes a POC implementation and two real-life use cases

A Review On Hadoop: Privacy For A Multi-Skyline Queries With Map Reduce

The significance of skyline  brings about numerous applications, for example, multi-criteria basic leadership, information mining, and data prescribed frameworks. Horizon inquiries are valuable for finding intriguing tuples from an extensive informational collection as indicated by different criteria. The sizes of informational collections are always expanding and the design of back-closes are changing from single-hub situations to non-traditional ideal models like MapReduce The horizon administrator has pulled in impressive consideration as of late because of its wide applications. In any case, processing a horizon is testing today since we need to manage huge information. For information concentrated applications, the MapReduce structure has been broadly utilized as of late. In this paper, also, we apply the strength control sifting technique to adequately prune non-horizon focuses ahead of time. We next parcel information in light of the areas separated by the quad tree and process competitor horizon focuses for each segment utilizing MapReduce.   At long last, MapReduce Grid Partitioning based Single-Reducer Skyline Computation (MR-GPSRS) utilizes a solitary reducer to amass the neighborhood horizons properly to figure the worldwide horizon. Conversely, MapReduce Grid Partitioning based Multiple Reducer Skyline Computation (MR-GPMRS) additionally separates neighborhood horizons and disperses them to different reducers that process the worldwide horizon in a free and parallel way. The proposed calculations are assessed through broad analyses, and the outcomes demonstrate that MR-GPMRS fundamentally beats the choices in different settings. we propose an effective technique for preparing multi-horizon inquiries with MapReduce with no alteration of the Hadoop internals. Through different analyses, we demonstrate that our approach beats past examinations by requests of extent

Web service management system for bioinformatics research: a case study.

In this paper, we present a case study of the design and development of a Web Service management system for bioinformatics research. The described system is a prototype that provides a complete solution to manage the entire life cycle of Web services in bioinformatics domain, which include semantic service description, service discovery, service selection, service composition, service execution, and service result presentation. A challenging issue we encountered is to provide the system capability to assist users to select the "right" service based on not only functionality but also properties such as reliability, performance, and analysis quality. As a solution, we used both bioinformatics and service ontology to provide these two types of service descriptions. A service selection algorithm based on skyline query algorithm is proposed to provide users with a short list of candidates of the \best" service. The evaluation results demonstrate the eciency and scalability of the service selection algorithm. Finally, the important lessons we learned are summarized and remaining challenging issues are discussed as possible future research directions

Service selection and transactional management for web service composition

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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

Deployment and Operation of Complex Software in Heterogeneous Execution Environments

This open access book provides an overview of the work developed within the SODALITE project, which aims at facilitating the deployment and operation of distributed software on top of heterogeneous infrastructures, including cloud, HPC and edge resources. The experts participating in the project describe how SODALITE works and how it can be exploited by end users. While multiple languages and tools are available in the literature to support DevOps teams in the automation of deployment and operation steps, still these activities require specific know-how and skills that cannot be found in average teams. The SODALITE framework tackles this problem by offering modelling and smart editing features to allow those we call Application Ops Experts to work without knowing low level details about the adopted, potentially heterogeneous, infrastructures. The framework offers also mechanisms to verify the quality of the defined models, generate the corresponding executable infrastructural code, automatically wrap application components within proper execution containers, orchestrate all activities concerned with deployment and operation of all system components, and support on-the-fly self-adaptation and refactoring