Early Experience Using Amazon Batch for Scientific Workflows

Abstract

Recent technological trends have pushed many products and technologies into the cloud, relying less on local computational services, and instead purchasing computation a la carte from cloud service providers. These providers focus more on delivering technologies which are service based rather than throughput based. With the advent of Amazon Batch, a new high throughput service, we wished to see how capable it was for running scientific workflows compared to existing cloud services. To that end, we developed a testing suite which created workflows focusing on increasing shared file sizes, increasing unique file sizes, and increasing number of tasks, and ran the workflows on Amazon Batch plus two other similar configurations for comparison: EC2 workers and Work Queue on EC2. We found that while there is a significant delay in sending jobs to Amazon Batch and running raw EC2 workers, there is little overhead in the actual running of the task, and similar performance to using Work Queue on EC2 when the workflow does not require large input files. Additionally, when performing real a workflow, Batch achieved a speedup over Work Queue workers on EC2 instances of 1.18x.1