A Genetic Algorithm for Scheduling Workflow Applications in Unreliable Cloud Environment

Cloud Computing refers to application and services offered over Internet using pay-as-you-go model. The services are offered from data centers all over the world, which jointly are referred to as the “Cloud”. The data centers use scheduling techniques to effectively allocate virtual machines to cloud applications. The cloud applications in area such as business enterprises, bio-informatics and astronomy need workflow processing in which tasks are executed based on data dependencies. The cloud users impose QoS constraints while executing their workflow applications on cloud. The QoS parameters are defined in SLA (Service Level Agreement) document which is signed between cloud user and cloud provider. In this paper, a genetic algorithm has been proposed that schedules workflow applications in unreliable cloud environment and meet user defined QoS constraints. A budget constrained time minimization genetic algorithm has been proposed which reduces the failure rate and makespan of workflow applications. It allocates those resources to workflow application which are reliable and cost of execution is under user budget. The performance of genetic algorithm has been compared with max-min and min-min scheduling algorithms in unreliable cloud environment.