A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds

Abstract

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.