Coevolutionary workflow scheduling in a dynamic cloud environment

Abstract

In this paper, we present a new coevolutionary algorithm for workflow scheduling in a dynamically changing environment. Nowadays, there are many efficient algorithms for workflow execution planning, many of which are based on the combination of heuristic and metaheuristic approaches or other forms of hybridization. The coevolutionary genetic algorithm (CGA) offers an extended mechanism for scheduling based on two principal operations: task mapping and resource configuration. While task mapping is a basic function of resource allocation, resource configuration changes the computational environment with the help of the virtualization mechanism. In this paper, we present a strategy for improving the CGA for dynamically changing environments that has a significant impact on the final dynamic CGA execution process.