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Abstract
• In the last few years, cloud computing has
emerges as a computational paradigm that
cables 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, database,
and applications) without having to deal with
implementation or configuration details.
Abstract
• In fact, there are many cloud computing
environment 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 oc concepts within a domain.
Abstract
• 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
decade enabled the advent of e-Science,
which is entirely carried out in computational
environments. The term “e-Science” is strictly
related silico experiments.
3.1 Introduction
• The development of technologies such as
grids 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. An
in silico experiment is conducted by a scientist,
who is responsible for managing the entire
experiment, which comprise composing,
executing, and analyzing it.
3.1 Introduction
• 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 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.
3.1 Introduction
• A scientific workload 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.
3.1 Introduction
• Simultaneously, in the last few years, cloud
computing emerged as a new computation
paradigm where web-based service 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.
3.1 Introduction
• The programs and data (that are fundamental parts
of scientific workloads) are moving from local
environments to the cloud. Foster et al. 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.”
3.1 Introduction
• 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
fundamental need for scientific applications,
since the scientists can be isolated from the
complexity of the environment, focusing only
on their in silico experiment.
3.1 Introduction
• 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. Several technologies, platforms,
applications, infrastructures, and standards have
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.
3.1 Introduction
• Taxonomies 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
scientific community to classify environments to
compare different cloud computing environment
that are available for use.
3.1 Introduction
• 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 envirnment.
3.2 scientific Workflows and e-Science
• This section presents the main definitions
regarding e-Science and scientific workflow
concepts. These concepts are presented along
with some important aspects to be considered
when modeling or executing scientific
experiments using cloud computing. These
aspects are used as a basis for elaborating the
classes of the cloud computing taxonomy.
3.2.1 Scientific Workflows
• According to the Workflow Management Coalition, a
workflow may be defined as “the automation of a
business process, in whole or part, during which
documents, information or tasks are passed from one
participant to another for action, according to a set of
procedural rules.” A workflow defines the order of
task invocation or conditions under which tasks must
be invoked and the task synchronization. This
definition is related to business workflow; however, it
can be exploited in the scientific domain, where tasks
will be related to scientific applications instead of
business ones. An example of scientific workflow is
presented in Fig. 3.1. This workflow is part of a real
deep water oil exploitation scientific experiment.
3.2.1 Scientific Workflows
3.2.2 Scientific Workflow Management
Systems
• Scientific Workflow Management Systems (SWfMSs)
are responsible for coordinating the invocation of
programs, either locally or in remote environments.
Many different SWfMSs can be found in the
literature. Although current SWfMSs have many
important characteristics and evolutions, according to
Weske et al, these SwfMS need to offer adequate
support for the scientist throughout the
experimentation process, including: (i) designing the
workflow through a guided interface; (ii) controlling
several variations of workflows; (iii) executing the
workflow in an efficient way; (iv) handling failure and
; (v) accessing, storing, and managing data.
3.2.2 Scientific Workflow Management
Systems
• Most of this support can be achieved using the cloud
computing paradigm. More specifically, efficient
execution of scientific experiments, as well as
management of the large amount of scientific data
produced by the experiment, is provided by the
computational infrastructure of cloud computing
environments. The next section presents some
important aspects for scientific experiments to be
considered when choosing a cloud computing
environment.
3.2.3 Important Aspects of In Silico
Experiments
• In silico experiments (that are usually modeled as
scientific workflows) have some important aspects to
be considered when being modeled or executed.
Many of these aspects should be taken into account
when choosing a supporting cloud computing
environment. Cloud computing environment present
some important characteristics that are related to
those aspects and may influence when scientists
choose a cloud environment to use. This section
presents these aspects (business model, privacy,
pricing, technological infrastructure, architecture,
access, and standards) as they guide us to choose the
classes of the proposed taxonomy.
3.2.3 Important Aspects of In Silico
Experiments
• One of the most important aspects for
scientific experiments is reproducibility. To
reproduce and validate an experiment,
scientists must have all available information
related to the experiment, including which
parameter values were used in each instance
of execution, the result (both final and
intermediary) produced during its execution.
This type of information is called provenance.
3.2.3 Important Aspects of In Silico
Experiments
• This data is stored in databases or via specialized services to
store provenance, thus handling failure and retaining data
integrity. Therefore, to achieve experiment reproducibility, the
supporting cloud computing environment should provide two
fundamental features, data storage and environment
configuration. Data storage is required store provenance data.
Preferably, there should be a service that provides storage or
database mechanisms to enable the scientists to access
provenance data and track how the result of an experiment
execution were obtained. Environment configuration is
required since the whole environment used to execute the
experiment should be able to reconfigured. Those
characteristics are related to the business model followed by a
cloud computing environment.
3.2.3 Important Aspects of In Silico
Experiments
• Privacy is also a major issue for the scientific
community. Usually, provenance data and
programs related to scientific experiment are
considered intellectual property and because
of that, they are not public until public until
the research is published in a scientific paper.
This way, the privacy aspect of cloud
environments must be analyzed when dealing
with scientific experiments.
3.2.3 Important Aspects of In Silico
Experiments
• Another important aspect to be considered is related
to pricing. Scientifics frequently use open-source and
community environments. This type of programs and
environments is freely available for general use, thus
contributing to the reproducibility of experiment
executions. The open-software culture of the
scientific community must be considered, since most
cloud environment environments are commercial,
which means that the service is paid for. Thus,
scientists should take into account the pricing of
enviroments.
3.2.3 Important Aspects of In Silico
Experiments
• The architecture characteristics of the
environment chosen to execute the
experiment should also be taken into account.
Scientific experiments need to be monitored
and controlled by scientists. This way, the
chosen cloud environment should provide
characteristics such as monitoring, as well as
individual control of an experiment execution
independent from others’ executions.
3.2.3 Important Aspects of In Silico
Experiments
• In many scenarios the execution of a whole
experiment requires running programs in
different technological platforms (operational
systems, database servers), requiring that the
cloud computing environment deals with
heterogeneity.
3.2.3 Important Aspects of In Silico
Experiments
• Another important aspects is related to
performance. These experiments usually need
high-performance computational environment
to run. Even using these environments,
experiments may need days, weeks, or even
months to finish. It is important to know (and
classify) the technology infrastructure involved
with the experiment to discover if this
technology is able to offer the necessary
computational resources to execute the entire
experiment.
3.2.3 Important Aspects of In Silico
Experiments
• Another important topic is related to how
scientifists access the cloud environment to
run experiments. The in silico scientific
experiment mus be able to access cloud
environments in different ways. For example,
in a specific experiment, result must be
provides in a web page through a web
browser; in another experiment, there must
be an API to control the execution of the
experiment, and so on.
3.2.3 Important Aspects of In Silico
Experiments
• In silico scientific experiments should be
based on standards, ideally already used on
the experiment domain or recommended by
entities such as W3C. These standards are
important when modeling an in silico scientific
experiment. Scientific experiments are usually
based on open standards. The next section
presents the proposed taxonomy for cloud
computing that takes into the account the
aspects listed in this section.
3.3 A Taxonomy for Cloud Computing
• A taxonomy is a particular classification
arranged in a hierarchical structure. It is
typically organized by a parent-child
relationship. Originally the term “taxonomy”
referred only to the classification of living
organisms. However, it has become popular in
certain domains of science to apply the term
in a wider, more general sense, where it may
refer to a classification of things or concepts.
3.3 A Taxonomy for Cloud Computing
• The cloud computing taxonomy presented in
this chapter provides the classification of the
components of the cloud computing domain
into categories based on different aspects of
this field and the requirements of scientific
experiment. This section describes a cloud
computing taxonomy (presented in Fig. 3.2),
which is decomposed into eight
subtaxonomies.
3.3 A Taxonomy for Cloud Computing
• The proposed taxonomy classifies the
characteristics of cloud computing in terms of
architectural characteristics, business model,
technology infrastructure, privacy, standards,
pricing, orientation, and access. Many of the
classes of the taxonomy are interrelated. In Fig.
3.2, these relations are represented in orange
arrows. Each one of these relations is
explained throughout the chapter.
3.3.1 Business Model
• According to the business model adopted,
clouds are usually classified into three major
categories (Fig. 3.3): Software as a Service
(SaaS), Platform as a Service (PaaS), and
infrastructure as a Service (IaaS), creating a
model named SPI.
3.3.1 Business Model
3.3.1 Business Model
• In SaaS, the software is deployed by a service
provider (just like an application to end-user) for
commercial or free use as a service on demand. In
IaaS, the provider delivers a computational
infrastructure (such as a supercomputer) to the enduser on the web. In IaaS, the end-user is usually
responsible for configuring the environment to use.
PaaS is the delivery of a programming environment as
a Service. The process of delivering platforms as
services facilitates the deployment of applications
into the cloud.
3.3.1 Business Model
• However, these three categories are to
generic. More classification levels are indeed
needed. For example, in the e-Science field,
the generated data is one of the most valuable
resources. This classification does not take
into account services that are based on
storage or database.
3.3.1 Business Model
• The business model subtaxonomy should
include the following areas: Storage as a
Service (StaaS) and Database as a Service
(DaaS), which are fundamental for e-Science
and Scientific workflows. We may define
Storage as a Service as a service that provides
structured ways to access and maintain a
storage facility that is remotely located.
However, this kind of business model provides
only the space and structure to store data.
3.3.1 Business Model
• In Scientific experiments, the scientific usually
need a database to store provenance data,
because a database provides feature such as
indexing and concurrency control, that is a
simple storage does not provide.
•
3.3.1 Business Model
• This way, Database as a Service (DaaS)
provides operations and functions of a
remotely hosted database, sharing it with
other users, and having it logically function as
if the database were local. This way, we may
see the Database-as-a-Service as one
specialization of Storage-as-a-Service.
3.3.1 Business Model
• The business model directly influences the
orientation of the cloud environment. For
example, an IaaS business model allows a
user-centric environment, since the user is in
control. On the other hand, as SaaS business
model does not. This class of the taxonomy is
essential to guarantee the reproducibility of
scientific experiments. The business model
directly defining if the cloud environment
offers data, infrastructure, or application as a
service, essential to guarantee reproducibility.
3.3.1 Business Model
• For example, there should be a way to store
provenance data to be further analyzed, thus
the cloud computing environment should
follow DaaS allow data storage.
3.3.2 Privacy
• According to the privacy aspect, we may
classify cloud environments as private, public,
and mixed (Fig. 3.4). Public clouds may be
considered as the most traditional of all types.
In this kind of cloud, the various resources are
dynamically provided over the Internet, via
web applications or web services, to any user.
Private clouds are environments that emulate
cloud computing on private networks, inside a
cooperation or scientific or a scientific
institution.
3.3.2 Privacy
3.3.2 Privacy
• A mixed cloud environment is one that is
composed by multiple public and/or private
clouds. The concept of mixed cloud is still
dubious. Some authors call a mixed cloud also
as hybrid. Although this term is not wrong, it
is also used to define clouds that are
implemented by different technologies, which
may cause confusion.
3.3.2 Privacy
• This class of the taxonomy is important for eScience because of the importance of privacy
levels in scientific experiments. Programs and
data are usually not public and scientists may
prefer not to install programs or store data in
public envirnments.
3.3.3 Pricing
• Since it is important for the scientific
experiments to deal with costs, we must
classify cloud environment according to a
pricing criterion. This subtaxonomy (Fig. 3.5) is
composed of three main types of pricing. Free
pricing is the pricing model applied when you
are using your own cloud environment, where
the resources are freely available for
authorized users.
3.3.3 Pricing
• The pay-per-use model is the one where the
user pays a specific value related to his
resource utilization. Also, it can be specialized
to a component-based prici, where each
component (storage, CPU, and so on) has s
different price and the real-time bill broken
down by exact usage of components.
3.3.3 Pricing
• These pay-per-use model are usually applied in
both commercial clouds and scientific clouds.
Science users pay for cloud usage in the same
way as commercial users do. To our
knowledge, there are no scientific institutions
that share their resources at no cost.
• Pricing is influenced access characteristics.
Since a cloud environment offers more access
methods, each one of them is a component
that can be priced by the provider.
3.3.4 Architecture
• This subtaxonomy (Fig. 3.6) classifies the main
architectural characteristics of a cloud
computing environment. One Fundamental
architectural aspect of a cloud is
heterogeneity. A cloud must support the
aggregation of heterogeneous hardware and
software resources, as it happens with
scientific experiments. The concept of
vituralization is also a key aspect for clouds.
3.3.4 Architecture
• Through virtualization, many users may benefit from
the same infrastructure using independent instances.
Virtualization enables the first security level in the
clouds, since it allows the isolation of environments.
In clouds, each user has unique access to its
individual virtualized environment. Resource sharing
is provided by clouds, since each resource is
represented as a single artifact, giving the impression
of a single dedicated resource. Scalability is mainly
defined by increasing the number of working nodes.
3.3.4 Architecture
• By definition, clouds offer the automatic
resizing of virtualized hardware resources.
Monitoring refers to the ability of watching
the current status of virtual machines or
services provided.
3.3.4 Architecture
• Each one of those architecture characteristics
is standardized by specific standards (which
are another class of the taxonomy). Besides
that, some architectural characteristics are
important to scientific experiments, such as
scalability and monitoring to control the
execution.