Optimization Scheduling for Scientific Applications with Different Priorities across Multiple Clouds

With the wide adoption of cloud computing, the scientific applications are migrated to cloud for execution. The complex structure of scientific applications bring challenges to optimization scheduling scientific applications across multiple heterogeneous clouds. In this paper, the directed acyclic graphs (DAGs) are adopted to represent scientific applications, which have different priorities in the process of scheduling. We propose a dynamic multi-cloud priority list scheduling algorithm (DMPLS), combining with workloads preemptive strategy and feedback mechanism to schedule scientific applications in time. Our algorithm regulates the workloads scheduling dynamically based on the updated information about the actual workloads execution time. The experimental results show that the proposed algorithm reduces the average time to complete the applications compared with First-Come-First-Service and Round-Robin algorithm. Moreover, the advantage of the DMPLS algorithm is more significant under the severe resources confliction situations.