For optimizing the economical cost and scheduling efficiency of cloud workflow scheduling, this paper proposed a workflow scheduling algorithm PBWS based on DAG(directed acyclic graph) partition. With the goal of optimizing synchronously the workflow scheduling efficiency and cost, this algorithm divided the scheduling solution into three stages: In the process of workflow DAG structure partition, partition structure adjustment and resource allocation. In the process of workflow DAG structure partition, it got the initial tasks partition graph when guaranteeing the execution order-dependency between tasks. In the process of partition structure adjustment, it re-allocated tasks in different partitions with a goal of reducing execution makespan. In the process of resource allocation, it determine the most cost-efficient matches between tasks and resources to ensure the minimization of the total idle time of resource. This paper constructed some simulation experiments for algorithms by the five types of scientific workflow DAG model. The experimental results show that PBWS algorithm can greatly reduce the execution cost of workflow in terms of cost by a little of overhead on execution makespan and realize the synchronous optimization of the scheduling efficiency and the scheduling cost, whose overall performance performs better than the same type of algorithms.

[2019-12-05] Printed Article /doc/2018.04.0375/v2