Minimum Cost Benchmarking for Intermediate Data Storage in Scientific Cloud Workflow Systems

Many scientific workflows are data intensive where a large volume of intermediate data is generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on cloud has become popular nowadays, more intermediate data in scientific cloud workflows can be stored by different storage strategies based on a pay-for-use model. In this paper, we build an Intermediate data Dependency Graph (IDG) from the data provenances in scientific workflows. With the IDG, deleted intermediate data can be regenerated, and as such we develop a novel algorithm that can develop a minimum cost storage strategy of the intermediate data in scientific cloud workflows systems. The strategy achieves the best trade-off of computation cost and storage cost by automatically storing the most appropriate intermediate datasets in the cloud storage. This strategy can be deemed as a minimum cost benchmark for all other intermediate data storage strategies in the cloud. We utilise Amazon’s cost model and apply the algorithm to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that benchmarking effectively demonstrates the cost effectiveness of other representative storage strategies.