Augmented Petri Net Cost Model for Optimisation of Large Bioinformatics Workflows Using Cloud

This paper concerns the trade-off that may be madebetween the cost of storing intermediate data and thecomputing costs incurred in regenerating this data when large bioinformatics or other workflows are implemented using cloud resources. The implementation may be required todelete some data to keep storage costs within a budget, anddeciding how best to do this with minimal increase incomputing costs can cause complex problems. To addressthese problems, a modified form of Petri net is introduced for modeling the workflow and allowing an optimization algorithm to be applied for addressing several types of problem that may arise. The proposed 'augmented Petri-net' simulates workflows with cost models included, thus providing a platform for an optimization procedure. Illustrations are presented to show that such optimization can achieve overall cost reductions in a number of different scenarios.