Dynamic resources configuration for coevolutionary scheduling of scientific workflows in cloud environment

Abstract

Modern composite scientific applications, also called scientific workflows, require large processing capacities. Cloud environments provide high performance and flexible infrastructure, which can be easily employed for workflows execution. Since cloud resources are paid in the most cases, there is a need to utilize these resources with maximal efficiency. In this paper we propose dynamic resources coevolutionary genetic algorithm, which extends previously developed coevolutionary genetic algorithm for dynamic cloud environment by changing computational capacities of execution nodes on runtime. This method along with using two types of chromosomes – mapping of tasks on resources and resources configuration – allows to greatly extend the search space of the algorithm. Experimental results demonstrate that developed algorithm is able to generate solutions better than other scheduling algorithms for a variety of scientific workflows.