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3.3.5 Technology Infrastructure • The technological infrastructure subtaxonomy (Fig. 3.7) is responsible to classify a cloud environment according to the computational power provided by cloud environments. Particular in commercial clouds, scientists have no access to the kind of technology that is used to implement it. In fact, in commercial cloud environments implementation details are hidden form the end-user (Scientist). On the other hand, in academic or private clouds it is possible to obtain this information. This information may be quite useful in e-Science because many experiments need a powerful computational environment to run and if the cloud environment is not able to provide powerful resources, it will not be able to support these experiments.
3.3.5 Technology Infrastructure • But, it is complicated to scientists to choose between those environments (clusters, blades, or grids) to run experiments, since they may not be computer experts. This way, we need to classify the environments using another classification.
3.3.5 Technology Infrastructure • This subtoxonomy provides a generic classification of cloud environments according to the support provided for high-performance computing (HPC). Since many experiments need HPC environments to run, cloud environments need to be classified according to these aspects. This way, cloud environments may be classified into HPC support and non-HPC support cloud environments are those where multicore programming is allowing, and non-HPC support are those in which this kind is not provided, for example.
3.3.6 Access • This subtaxonomy (Fig. 3.8) classifies cloud environments according to its access types. In most cases, we may find four types of accesses: web browsers, thin clients, mobile clients, and API. Browsers are the most common access way for cloud services. Many applications and infrastructures are accessible only on web browsers. It is intuitive since almost since almost every computer has at least one browser installed and may access cloud services. Thin clients and mobile are types of access to clouds out of a desktop within handhelds or mobile phones. It has become popular to access service through phones instead of desktops. And finally, API is a fundamental way for accessing clouds.
3.3.6 Access • API is fundamental artifact for access through programming languages such as Java, Python, or C. By using an API, more complex application may use cloud infrastructure in a native form. Since the scientific experiments modeled as scientific workflows are enacted using SWfMSs, one important need to connect SWfMSs to clouds is using an API because an API can be easily invoked by programmable components and most of the scientists follow this tendency (潮流).
3.3.7 Standards • The subtaxonomy (Fig. 3.9) presents some categories and standards found on literature for cloud computing. The Extensible Messaging and Presence Protocol (XMPP) is an open technology for real-time communication, which powers a wide range of applications. Hyper Text Transfer Protocol (HTTP) is the most known standard for communication and it is intuitive to use it on the cloud, since it is used on basic web applications.
3.3.7 Standards • API authorization in a simple and standard way. On the other hand, OpenID is an open, decentralized standard for user authentication (確證) and access control, allowing users to log into many service with the same digital identity (身份), as adopted on grid. In addition, we may find SAML, which is a major player in cloud-based systems.
3.3.7 Standards • Atom Publishing Protocol (or simply Atom) is a content licensing protocol based on HTTP for creating and updating web resources. RSS must be included as a syndication (聯合) standard as well. Even RSS is not a recommended standard but a de facto standard. As highlighted on the scientific experiments requirements, security is a key aspect and virtualization is a key aspect of cloud computing and needs some standards.
3.3.7 Standards • The OVF (Open Virtualization Format) is being considered as one of de factor standards for virtualization. OVF enables flexible and secure distribution on software and data, facilitating the mobility of virtual machines. As happens in many more XML-based languages such as XSML, XACML, and JSON is lightweight data interchange format.
3.3.8 Orientation • One important aspect of cloud computing for e-Science is the orientation (taxonomy represented in Fig. 3.10). Usually, the orientation changes as the type of service changes. For instance, when an application is provided on the cloud, we may consider it task-centric, because it is oriented to the task that will be executed. In other words, you need to transfer control to the application owners instead of having control of it. However, when the infrastructure is provided as a service, the user has control of the process. The programs, applications, and data are chosen by the user. Thus, the cloud may be user-centric.
3.4 Classifying Cloud Computing Environments Using the Taxonomy • In this section, we present a summarized survey of the main existing cloud computing environments according to the proposed taxonomy. Table 3.1 shows the selected cloud computing environments with their categorization based on the taxonomy. These cloud computing environments are the most commonly found in scientific literature.
3.4 Classifying Cloud Computing Environments Using the Taxonomy
3.4 Classifying Cloud Computing Environments Using the Taxonomy • In Table 3.1, we may observe that none of analyzed environments provides all functionalities and characteristics presented in the proposed taxonomy. Scientists will have to analyze their needs and verify in the classification the environment that is the most suitable. For example, suppose that scientific experiments require HPC support, API, and privacy as its main requirements. In a first analysis, scientists would choose between Nimbus and Eucalyptus. However, if a database service is also an important issue to be considered, they trade between the available environments.
Taxonomies for Cloud Computing • There are some proposals in the literature related to cloud computing taxonomies. All presented taxonomies have focused mostly on the commercial aspect (e.g., business model), lacking on describing the domain according to important aspects for e-Science such as standards, privacy levels, and so on. Cloud computing providers adopt a specialized taxonomy to explain their approach, especially if they have to distinguish themselves from others. This section presents four taxonomies, already developed for the cloud computing domain.
Taxonomies for Cloud Computing • Youseff proposes a unified ontology (實體論) for cloud computing. It presents a summary of cloud computing components, with a classification of these components, and their relationship. Even though this paper is a step forward, highlighting many technical challenges involved in building cloud components, it is not a real ontology (實體論), but a taxonomy that partially covers the cloud computing domain.
Taxonomies for Cloud Computing • In fact, this work classifies just the cloud computing components in five main layers. In addition, this ontology (實體論) only takes the business model into account (classifying cloud computing as software as a service, hardware as a service, and so on). Many other aspects are needed to classify cloud computing environment, particularly for e-Science, such as pricing, access methods, and so on.
Taxonomies for Cloud Computing • Leavitt, presents the whole cloud scenario with advantages and disadvantages, explaining the adoption of cloud companies around the world and classifying cloud computing environments into four types that are equivalent to the business models presented in this paper. However, it proposes a type called “general services” that consider databases and storage provides as a service, differently from our taxonomy that created a new type named DaaS to designate this type of business model.
Taxonomies for Cloud Computing • This classification may be too generate since it groups in one class (general services) many important types for e-Science. Services for different purposes are classified as the same, this may be desirable.
Taxonomies for Cloud Computing • Laird classifies cloud environments in taxonomy that is composed by four main classes: Infrastructure, Platform, Service, and Applications. In each of these classes, it details some aspects and presents cloud environments that correspond to the classification. Many of the classes used in this work are present in our taxonomy. However, it is not focused on e-Science aspects and many classes are not considered. Laid is focused on commercial environments, and because of that, some classification is missing, such as HPC supporting. Since it is not fundamental aspect for commercial applications that are executed in clouds, it was nor considered.
Taxonomies for Cloud Computing • The United States National Institute of Standards and Technology (NIST) recently provided definitions for cloud computing through an implicit taxonomy. However, different from the taxonomy presented in this chapter, the NIST cloud computing taxonomy has focused on the business model aspect, lacking on describing the domain according to different aspects such as standards, privacy levels, and so on.
3.6 Conclusions and Final Remarks • In this chapter, we have introduced a taxonomy for cloud computing from an e-Science perspective. The authors believe that it will be useful for the scientific community in evaluating and comparing different cloud environments. By classifying environments using the proposed taxonomy, they may evaluate which environments meet their needs for executing scientific experiments in clouds. Different from the existing taxonomy considers a broad view of cloud computing according to important aspects of scientific experiments and aims to explore the major properties of it.
3.6 Conclusions and Final Remarks • This chapter highlights that despite the high interest about the topic, it is still a wide open field. New solutions for cloud computing are available, and many others are being announced, which makes the cloud computing field very fertile and hard to be understood and classified. It is fundamental that scientists are able to choose the best cloud environment for their experiments. The use of the taxonomy and its common vocabulary may facilitate scientists to find common characteristics of the existing environments and may help them to choose the most adequate one.
