Parallel Execution of Data-intensive Web Services Based on Data-flow Constructs and I/O Operation Ratio

Distributed computing which uses Web services as fundamental elements, enables high-speed development of software applications through composing many interoperating, distributed, re-usable, and autonomous services. As a fundamental challenge for service developers, service composition must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. On the other hand, huge amounts of data have been created by advances in technologies, which may be exchanged between services. Data-intensive Web services are of great interest to implement data-intensive processes. However, current approaches to Web service composition have omitted either the effect of data, or the distribution of services. Evolutionary Computing (EC) techniques allow for the creation of compositions that meet all the above factors. In this paper, we will develop Genetic Algorithm (GA)based approach for solving the problem of distributed data-intensive Web service composition (DWSC). In particular, we will introduce two new heuristics, i.e. LCS (Longest Common Subsequence) and distance of services, in designing crossover operators. Additionally, a new local search technique incorporating distance of services will be proposed.

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Parallel Execution of Data-intensive Web Services Based on Data-flow Constructs and I/O Operation Ratio

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Cited by 2 publications

References 22 publications

Distributed and scalable sequential pattern mining through stream processing

Distributed and scalable sequential pattern mining through stream processing

Cluster-Guided Genetic Algorithm for Distributed Data-intensive Web Service Composition

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