ACOsched: A scheduling algorithm in a federated cloud infrastructure for bioinformatics applications

Task scheduling in a federated cloud environment is a complex problem since there are several cloud providers presenting distinct memory and storage capacities that should be addressed. This article focus on the task scheduling problem in BioNimbuZ, a federated cloud infrastructure for executing bioinformatics applications, which was previously proposed by our group. We present a scheduling algorithm based on Load Balancing Ant Colony (LBACO), called ACOsched, to perform efficient distribution of tasks by finding the best cloud in the federation to execute these tasks. We developed experiments using real biological data, executing the Bowtie mapping tool on one instance of BioNimbuZ, composed by two cloud providers, Amazon EC2 and a bioinformatics laboratory at the University of Brasilia/Brazil. The obtained results show that ACOsched led to a significant improvement in the makespan time of Bowtie executing in BioNimbuZ, when compared to the simple round robin algorithm called DynamicAHP, previously developed in this federated cloud infrastrucutre.

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