High-Performance Cloud Computing: A View of Scientific Applications

TL;DR

This paper discusses how cloud computing, exemplified by Aneka, offers a flexible, scalable, and cost-effective platform for scientific applications, demonstrated through case studies in gene expression classification and brain imaging workflows.

Contribution

It introduces Aneka, a cloud platform supporting diverse scientific computing scenarios with dynamic resource provisioning and SLA management.

Findings

Aneka effectively supports scientific workflows in cloud environments.

Cloud computing reduces setup and maintenance costs for scientific computing.

Preliminary case studies demonstrate Aneka's applicability in bioinformatics and neuroimaging.

Abstract

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.