Cloud computing can provide a more cost-effective way to deploy scientific workflows than traditional distributed computing environments such as cluster and grid. Due to the large size of scientific datasets, data placement plays an important role in scientific cloud workflow systems for improving system performance and reducing data transfer cost. Traditional task-level data placement strategy only considers shared datasets within individual workflows to reduce data transfer cost. However, it is obvious that task-level strategy is not necessarily good enough for the situation of multiple workflows at the workflow level. In this paper, a novel workflow-level data placement model is constructed, which regards multiple workflows as a whole. Then, a two-stage data placement strategy is proposed which first pre-allocates initial datasets to proper datacenters during workflow build-time stage, and then dynamically distributes newly generated datasets to appropriate datacenters during runtime stage. Both stages use an efficient discrete particle swarm optimization algorithm to place flexible-location datasets. Comprehensive experiments demonstrate that our workflow-level data placement strategy can be more cost-effective than its task-level counterpart for data-sharing scientific cloud workflows.

The software-defined 5G networking is one of the most promising 5G network architectures. Software Defined Networking (SDN) is an essential technology in such network architecture, which interconnects the virtualized network functions and is responsible for creating data forwarding paths. In this paper, we propose a practical data forwarding path selecting method to effectively select high-quality main and backup paths. Specifically, we propose a modified LKH algorithm by adopting directed K-OPT operations to find a valid route path. Then, we introduce a single path searching algorithm and a dual path searching algorithm by combining a complexity reducing technique called node scaling and two types of penalties. The comparative results over a HUAWEI official data set and a real network data set demonstrate that the proposed path selecting method, compared with previous path selecting methods, achieves a much better overall performance in terms of the least overlapping directed edges, low total path cost and minimal path searching time.