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

被引：17

作者：

Sadeghiram, Soheila ^{[1
]}

Ma, Hui ^{[1
]}

Chen, Gang ^{[1
]}

机构：

[1] Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington, New Zealand

来源：

2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2018年

关键词：

Web service composition; Evolutionary computing; distributed data-intensive Web services; network attributes; communication model; QOS; COST;

D O I：

10.1109/CEC.2018.8477729

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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, meta-heuristics 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.

引用

页码：2317 / 2323

页数：7

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