Multi-Resource Partial-Ordered Task Scheduling in cloud computing

In this paper, we investigate the scheduling problem with multi-resource allocation in cloud computing environments. In contrast to existing work that focuses on flow-level scheduling, which treats flows in isolation, we consider dependency among subtasks of applications that imposes a partial order relationship in execution. We formulate the problem of Multi-Resource Partial-Ordered Task Scheduling (MR-POTS) to minimize the makespan. In the first stage, the proposed Dominant Resource Priority (DRP) algorithm decides the collection of subtasks for resource allocation by taking into account the partial order relationship and characteristics of subtasks. In the second stage, the proposed Maximum Utilization Allocation (MUA) algorithm partitions multiple resources among selected subtasks with the objective to maximize the overall utilization. Both theoretical analysis and experimental evaluation demonstrate the proposed algorithms can approximately achieve the minimal makespan with high resource utilization. Specifically, a reduction of 50% in makespan can be achieved compared with existing scheduling schemes.