A load-balancing approach based on modified K-ELM and NSGA-II in a heterogeneous cloud environment

MapReduce is a popular programming model widely used in distributed systems. With regard to large-scale applications, e.g. home energy management in a city, online social community etc., load-balancing becomes critical affecting the performance of distributed computing. Present proposed load-balancing approaches in MapReduce aim at optimizing task execution time, whereas disk space is not considered. In this paper, a new scheme which consists of modified K-ELM and NSGA-II is proposed. Corresponding experiment results have shown that our method can assign tasks evenly, and effectively improve the performance of a cloud system.