In distributed computing,Web Service Composition (WSC)leads to the effective reuse of existing services and produces added value. WSC must fulfil functional requirements and optimiseQuality of Service (QoS)attributes, simultaneously.Memetic Algorithms (MAs)are promising for automatically composing numerous Web services to satisfy the above requirements.Data-intensive Web servicesfocus on providing and updating data with a significant volume of data operation and exchange. However, current composition approaches have ignored the impact of data communication and the distribution of services, which significantly affect the performance when applied to the challengingDistributed Data-intensive Web Service Composition (DDWSC)problem. Although recent approaches have revealed the usefulness of local search, they have completely overlooked the question of preferring appropriate composition solutions for local search. To address this research issue, we propose a priority-based selection method for the local search that can be consistently integrated with any MA for DDWSC. This enables us to develop state-of-the-art algorithms for DDWSC by explicitly considering the problem-specific, population and solution-related information for choosing a solution. Extensive experimental evaluation using benchmark datasets shows that our proposed method significantly outperforms several recently proposed methods.

Web Service Composition (WSC) is a particularly promising application of Web services, where multiple individual services with specific functionalities are composed to accomplish a more complex task, which must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. Additionally, large quantities of data, produced by technological advances, need to be exchanged between services. Data-intensive Web services, which manipulate and deal with those data, are of great interest to implement data-intensive processes, such as distributed Data-intensive Web Service Composition (DWSC). Researchers have proposed Evolutionary Computing (EC) fully-automated WSC techniques that meet all the above factors. Some of these works employed Memetic Algorithms (MAs) to enhance the performance of EC through increasing its exploitation ability of searching neighbourhood area of a solution. However, those works are not efficient or effective. This paper proposes an MA-based approach to solving the problem of distributed DWSC in an effective and efficient manner. In particular, we develop an MA that hybridises EC with a flexible local search technique incorporating distance of services. An evaluation using benchmark datasets is carried out, comparing existing state-of-the-art methods. Results show that our proposed method has the highest quality with an acceptable execution time.

Automatic Web service composition has received much interest in the last decades. Data-intensive concepts have provided a promising computing paradigm for data-intensive Web service composition. Due to the complexity of the problem, metaheuristics in particular Evolutionary Computing (EC) techniques have been used for solving this composition problem. However, most of the current works neglected the distributed nature of data-intensive Web services. In this paper, we study the problem of distributed data-intensive service composition and propose a model which integrates attributes of constituent data-intensive Web services and attributes of the network. The core idea is to propose a communication cost and time model of a composed Web service considering communication delay and cost. We therefore propose a novel method based on Genetic Algorithm (GA) which uses a variation of K-means clustering algorithm.