Task scheduling in heterogeneous environment is a NP problem

it is concerned about the large-scale resource and task scheduling

requires the scheduling algorithm is highly efficient. As the number of tasks and resources to increase the number of genetic algorithm to show the shortcomings of slow convergence. In order to overcome its shortcomings

this paper improved genetic algorithm based on the increased packet processing and load balancing strategy

a hybrid genetic scheduling policy

simulation experiments show that the hybrid genetic algorithm-based scheduling policy than the traditional scheduling strategy better performance

the algorithm more complex

heterogeneous environment

the system can better meet the characteristics of time and resources to minimize the overhead issue.