Scheduling Workflows With Composite Tasks: A Nested Particle Swarm Optimization Approach

Scientific cloud workflows enable the access to distributed computing resources in cloud environments for executing scientific computing applications. In the literature, most workflow scheduling models assume that each workflow task is mapped to only one service instance. But in computation and data-intensive applications, it is common that the computation resources provided by a single service instance are insufficient for some complicated tasks which contain several closely correlated sub-tasks. To manage such complicated workflows, this article devises a novel workflow model with composite tasks (cWFS). The model views a complicated task as a composite task and allows mapping multiple service instances to a composite task. The data transmission among sub-tasks of a composite task can also be addressed by the proposed model. To solve cWFS problem, we devise a nested particle swarm optimization (N-PSO) that utilizes two kinds of populations, i.e., the outer population and inner population. Since N-PSO is a bit time-consuming, we further devise a Fast version of N-PSO (FN-PSO), which can save more than 60 percent of running time compared with N-PSO. The proposed approaches are evaluated on five real-world workflow types. The experimental results verify that the proposed approaches can solve the new workflow model effectively.

Automated summary

This article proposes a novel workflow model with composite tasks, which can manage complex workflows and address data transmission between sub-tasks. To solve this problem, a nested particle swarm optimization and a fast version of nested particle swarm optimization are devised.