Data Sharing Options for Scientific Workflows on Amazon EC2

TL;DR

This paper explores data management strategies for scientific workflows on Amazon EC2, analyzing performance and costs of different storage options through experiments with typical applications.

Contribution

It provides an empirical evaluation of various data sharing and storage options for scientific workflows in cloud environments, highlighting challenges and performance trade-offs.

Findings

Different storage options impact workflow performance and cost.

Deployment issues on EC2 affect data management efficiency.

Performance varies significantly with storage choice.

Abstract

Efficient data management is a key component in achieving good performance for scientific workflows in distributed environments. Workflow applications typically communicate data between tasks using files. When tasks are distributed, these files are either transferred from one computational node to another, or accessed through a shared storage system. In grids and clusters, workflow data is often stored on network and parallel file systems. In this paper we investigate some of the ways in which data can be managed for workflows in the cloud. We ran experiments using three typical workflow applications on Amazon's EC2. We discuss the various storage and file systems we used, describe the issues and problems we encountered deploying them on EC2, and analyze the resulting performance and cost of the workflows.