ACO-DPDGW: an ant colony optimization algorithm for data placement of data-intensive geospatial workflow

Massive data transmission between distributed data centers is the major efficiency bottleneck of geospatial workflow. Although many data placement methods have been proposed to overcome this problem, few researches have considered the impact of the structure of the workflow. In this paper, we define the problem of data placement for data-intensive geospatial workflow aiming to minimize the data transfer time. An algorithm called ant colony optimization based data placement of data-intensive geospatial workflow (ACO-DPDGW) is proposed to handle this problem. By taking advantage of the node vector to represent the traditional workflow model, the ants could place datasets and tasks in appropriate data centers according to the combination of pheromone information and heuristic information, when they visit the nodes randomly. To prevent premature convergence, a variable neighborhood search operation is embedded into ACO-DPDGW. The experiments show that our algorithm can reduce data transfer volume and data transfer time even as the numbers of datasets, tasks, and data centers increase.