Several studies have been carried out to check limitations of clouds in providing support to scientific applications. The major part is dedicated to the behavior of scientific applications, most of them characterized by large amounts of information processing and massive use of computational resources. In this context clouds emerge in providing additional resources, or in minimizing cost in the acquisition of new resources.

The use of clouds in support to scientific applications have inherented characteristics, different from the commercial ones. The virtualization technologies, are the basic elements of clouds’ infrastructure, and despite of their significant advances they still present limitations when confronted with the needs of high computational power and communication, demanded by several scientific applications. However, using virtualized resources demands a deeper understanding of the characteristics of the applications and the cloud architecture.

Our group of Distributed Scientific Computing at the National Laboratory for Scientific Computing (ComCidis / LNCC) and other research groups, suggest that different virtualization layers and hardware architecture used in the cloud infrastructure, influence the performance of scientific applications.

This influence leads to the concept of affinity, i.e., which group of scientific applications has a better performance associated to the virtualization layer and hardware architecture beeing used. These aspects involve: a) to reduce cloud environment limitations in support to scientific applications; b) to provide the basis for the development of new cloud scheduling algorithms; c) to assist the acquisition of new resources and cloud providers, looking for performance and resource usage optimization.

Currently, the ComCiDis group is developing a set of research projects aiming to understand the relationship among: scientific applications, virtualization layers and infrastructure, based on its private development cloud platform named Neblina. The platform should enable prospecting new technologies and solutions in optimizing the use of cloud environments.


Bruno Schulze Computer Science Senior Research Scientist

From Processing Units to Computational Ecosystems to the Cloud e-session

Photos by : David Rytell