文章基本信息

标题：A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds
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作者：Weishan Zhang ; Shouchao Tan ; Qinghua Lu 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/463230
出版社：Hindawi Publishing Corporation
摘要：Pervasive computing is converging with cloud computing which becomes pervasive cloud computing as an emerging computing paradigm. Users can run their applications or tasks in pervasive cloud environment in order to gain better execution efficiency and performance leveraging powerful computing and storage capacities of pervasive clouds through task migration. During task migration, there are possibly a number of conflicting objectives to be considered when making migration decisions, such as less energy consumption and quick response, in order to find an optimal migration path. In this paper, we propose a genetic algorithms- (GAs-) based approach which is effective in addressing multiobjective optimization problems. We have performed some preliminary evaluations of the proposed approach which shows quite promising results, using one of the classical genetic algorithms. The conclusion is that GAs can be used for decision making in task migrations in pervasive clouds.