Workflow Scheduling for Remote Sense Application Using Data Transformation Graph in Inter-cloud

Inter-cloud environment provides massive computing resources to meet the increasing amount of data and computation for remote sensing applications. However, how to effectively map sub-tasks to different cloud service providers to achieve QoS(Quality of Service) index optimization is still a problem that needs to be studied. Remote sensing applications need to process trillions of bytes of data each time, unreasonable scheduling will lead to a large amount of data transmission across the cloud, which will seriously affect the performance of QoS indicators such as makespan and cost. By using data transformation graph(DTG) to study the data transmission process of global drought detection application, which is a remote sensing application, an optimized scientific workflow scheduling based on genetic algorithm is proposed for inter-cloud environment, which can minimize makespan and cost simultaneously. Experimental results show that this method can significantly optimize QoS index for data-intensive application like remote sense application and can effectively reduce the impact of performance bottlenecks.