Cost-based multi-QoS job scheduling algorithm using genetic approach in cloud computing environment
Ratin Gautam, Shakti Arora
One of the best methods for cloud scheduling is the genetic algorithm (GA). The simple and parallel features of this algorithm make it applicable to several optimization problems. A GA searches the problem space globally and is unable to search locally. In the proposed model, the task scheduler calls the GA scheduling function every task scheduling  cycle. This function creates a set of task schedules and evaluates the quality of each task schedule with user satisfaction and virtual machine  availability. The function iterates genetic operations to make an optimal task schedule. In the presented work, task scheduling is done to reduce the total cost of task processing (on the processing units of the cloud) for the cloud provider by reducing the execution time and hence the delay cost or penalty cost.