  In multi-cloud environment, task scheduling has attracted a lot of attention
due to NP-Complete nature of the problem. Moreover, it is very challenging due
to heterogeneity of the cloud resources with varying capacities and
functionalities. Therefore, minimizing the makespan for task scheduling is a
challenging issue. In this paper, we propose a genetic algorithm (GA) based
approach for solving task scheduling problem. The algorithm is described with
innovative idea of fitness function derivation and mutation. The proposed
algorithm is exposed to rigorous testing using various benchmark datasets and
its performance is evaluated in terms of total makespan.
