Chapter 3

Towards a Taxonomy for Cloud Computing from an e-Science Perspective

Daniel de Oliveira, Fernanda Araujo Baião, and Marta Mattoso

Abstract  In the last few years, cloud computing has emerged as a computational paradigm that enables scientists to build more complex scientific applications to manage large data sets or high-performance applications, based on distributed resources. By following this paradigm, scientists may use distributed resources (infrastructure, storage, databases, and applications) without having to deal with implementation or configuration details. In fact, there are many cloud computing environments already available for use. Despite its fast growth and adoption, the definition of cloud computing is not a consensus. This makes it very difficult to comprehend the cloud computing field as a whole, correlate, classify, and compare the various existing proposals. Over the years, taxonomy techniques have been used to create models that allow for the classification of concepts within a domain. The main objective of this chapter is to apply taxonomy techniques in the cloud computing domain. This chapter discusses many aspects involved with cloud computing that are important from a scientific perspective. It contributes by proposing a taxonomy based on characteristics that are fundamental for scientific applications typically associated with the cloud paradigm.

3.1  Introduction

The evolution of computer science in the last decades enabled the advent of e-Science, which is entirely carried out in computational environments. The term “e-Science” is strictly related to those experiments based on computer simulations that are known as silico experiments [27].

The development of technologies such as grids [6] fostered the popularity of e-Science and consequently in silico experiments. In silico experiments are commonly found in many scientific domains, such as oil exploration [20]. An in silico

D. de Oliveira (*)

COPPE, Federal University of Rio de Janeiro, 21945-970, Rio de Janeiro-RJ-Brazil e-mail: danielcmo@gmail.com

Systems and Applications, Computer Communications and Networks,

DOI 10.1007/978-1-84996-241-4_3, © Springer-Verlag London Limited 2010

experiment is conducted by a scientist, who is responsible for managing the entire experiment, which comprises composing, executing, and analyzing it. Currently, most of the work of scientists during an in silico experiment is related to the execution of a sequence of programs. Each program produces a collection of data with certain semantics. These data are used as input to the next program to be executed in the chain sequence. The chaining of these programs may become unfeasible without systematic computational support. A scientific workflow may be defined as an abstraction that allows the structured composition of programs and data as a sequence of operations aiming at a desired result as defined by Mattoso et al. [16].

Simultaneously, in the last few years, cloud computing [28] emerged as a new computational paradigm where web-based services enabled different kinds of users to obtain a huge variety of capabilities, in infrastructure, software, and hardware, without having to deal with configuration and implementation details.

The programs and data (that are fundamental parts of scientific workflows) are moving from local environments to the cloud. Foster et al. [7] examined the differences between grid and cloud computing, offering a good foundation to categorize the existing cloud computing projects and/or services. They define cloud computing as “A large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet.”

The main advantage of cloud computing is that the average user is able to access a great variety of resources without having to acquire or configure the whole infrastructure. This is a fundamental need for scientific applications, since the scientists can be isolated from the complexity of the environment, focusing only on their in silico experiment.

The volume of published white papers and scientific papers evidences that cloud computing has both emerged and is already being adopted by some scientific projects [15]. Several technologies, platforms, applications, infrastructures, and standards have already been proposed. However, the concepts involved with cloud computing are not fully detailed or explained. Considering the growing interest in cloud computing and the difficulty in finding organized definitions of concepts associated to this paradigm, we present in this chapter a taxonomy for the cloud computing from an e-Science perspective.

Taxonomies [4] are a particular classification structure where concepts are arranged in a hierarchical way. The proposed cloud taxonomy provides an understanding of the domain and aims to help scientists when comparing different cloud computing environments. The cloud computing e-Science taxonomy presented in this chapter is useful for the scientific community to classify environments and to compare different cloud computing environments that are available for use. By consulting this taxonomy, they may consider the features that meet their needs, which may vary depending on the scientific experiment being conducted. The taxonomy considers a broad view of cloud computing, comprising all its major issues. Using the proposed taxonomy as a common vocabulary may facilitate scientists to find common characteristics of the existing environments and may help to choose the most adequate cloud environment.