A Storage Policy for a Hybrid Federated Cloud platform: A Case Study for Bioinformatics

Bioinformatics tools require large-scale processing mainly due to very large databases achieving gigabytes of size. In federated cloud environments, although services and resources may be shared, storage is particularly difficult, due to distinct computational capabilities and data management policies of several separated clouds. In this work, we propose a storage policy for BioNimbuZ, a hybrid federated cloud platform designed to execute bioinformatics applications. Our storage policy, BioClouZ, aims to perform efficient choices to distribute and replicate files to the best available cloud resources in the federation in order to reduce computational time. BioClouZ uses four parameters - latency, uptime, free size and cost, weighted (according to ad hoc tests) to model their influences to data storage and recovery. Experiments were performed with real biological data executing a commonly used tool to map short reads in a reference genome in BioNimbuZ, composed of clouds executing in Amazon EC2, Azure and University of Brasilia. The results showed that, when compared to the greedy algorithm first used in BioNimbuZ, the BioClouZ policy significantly improved the total execution time due to more efficient choices of the clouds to store the files. Other bioinformatics applications can be used with BioClouZ in BioNimbuZ as well, since the platform was designed independently from particular tools and databases.