Analysis of Scientific Cloud Computing requirements

TL;DR

This paper evaluates how the Infrastructure-as-a-Service cloud model can meet scientific computing needs, addressing challenges and potential benefits of adopting cloud features in scientific research environments.

Contribution

It analyzes the feasibility of IaaS cloud models for scientific applications and discusses key challenges and requirements for effective implementation.

Findings

Cloud features like elasticity can benefit scientific computing

Virtualization in scientific centers faces perception challenges

Addressing specific drawbacks can enhance cloud adoption in science

Abstract

While the requirements of enterprise and web applications have driven the development of Cloud computing, some of its key features, such as customized environments and rapid elasticity, could also benefit scientific applications. However, neither virtualization techniques nor Cloud-like access to resources is common in scientific computing centers due to the negative perception of the impact that virtualization techniques introduce. In this paper we discuss the feasibility of the IaaS cloud model to satisfy some of the computational science requirements and the main drawbacks that need to be addressed by cloud resource providers so that the maximum benefit can be obtained from a given cloud infrastructure.