Fruit fly optimization algorithm for network-aware web service composition in the cloud

Service Oriented Computing (SOC) provides a framework for the realization of loosely coupled service oriented applications. Web services are central to the concept of SOC. Currently, research into how web services can be composed to yield QoS optimal composite service has gathered significant attention. However, the number and spread of web services across the cloud data centers has increased, thereby increasing the impact of the network on composite service performance experienced by the user. Recently, QoS-based web service composition techniques focus on optimizing web service QoS attributes such as cost, response time, execution time, etc. In doing so, existing approaches do not separate QoS of the network from web service QoS during service composition. In this paper, we propose a network-aware service composition approach which separates QoS of the network from QoS of web services in the Cloud. Consequently, our approach searches for composite services that are not only QoS-optimal but also have optimal QoS of the network. Our approach consists of a network model which estimates the QoS of the network in the form of network latency between services on the cloud. It also consists of a service composition technique based on fruit fly optimization algorithm which leverages the network model to search for low latency compositions without compromising service QoS levels. The approach is discussed and the results of evaluation are presented. The results indicate that the proposed approach is competitive in finding QoS optimal and low latency solutions when compared to recent techniques

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

A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm

With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA) is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE) algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition

Novel optimization schemes for service composition in the cloud using learning automata-based matrix factorization

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyService Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment